mind wandering task related thought

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8 The Philosophy of Mind-Wandering

Zachary C. Irving Departments of Philosophy and Psychology University of California, Berkeley Berkeley, California, United States

Evan Thompson Department of Philosophy University of British Columbia Vancouver, British Columbia, Canada

• Published: 05 April 2018
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This chapter provides an introduction to the philosophy of mind-wandering. It begins with a philosophical critique of the standard psychological definitions of mind-wandering as task-unrelated thought or stimulus-independent thought. Although these definitions have helped bring mind-wandering research onto center stage in psychology and cognitive neuroscience, they have substantial limitations. They do not account for the dynamics of mind-wandering, task-unrelated thought that does not qualify as mind-wandering, or the ways in which mind-wandering can be task-related. The chapter reviews philosophical accounts that improve upon the current psychological definitions, in particular an account of mind-wandering as “unguided thinking.” It critically assesses the view that mind-wandering can be defined as thought lacking meta-awareness and cognitive agency, as well as the view that mind-wandering is disunified thinking. The definition of mind-wandering as unguided thinking not only is conceptually and phenomenologically precise, but also can be operationalized in a principled way for empirical research.

Before the twenty-first century, research on the wandering mind was “relegated to the backwaters of mainstream empirical psychology” ( Smallwood & Schooler, 2006 , p. 956). Not anymore. Indeed, some researchers have dubbed our time “the era of the wandering mind” ( Callard, Smallwood, Golchert, & Margulies, 2013 ). Nevertheless, because the cognitive science of mind-wandering is so young, foundational questions remain unanswered. In particular, there is no consensus about how to define mind-wandering ( Christoff, 2012 ; Irving, 2016 ), although recent philosophical work on mind-wandering has addressed this foundational issue ( Carruthers, 2015 ; Dorsch, 2015 ; Irving, 2016 ; Metzinger, 2013 , 2015 ; Sutton, 2010 ; Thompson, 2015 ). In this chapter, we provide an introduction to the philosophy of mind-wandering, and we argue that mind-wandering is best defined as “unguided thinking” ( Irving, 2016 ).

We begin by criticizing the standard definitions of mind-wandering in psychology, according to which mind-wandering is “task-unrelated thought” or “stimulus-independent thought” (see Irving, 2016 ). Scientists have used these definitions to produce important findings and bring mind-wandering into center stage in psychology and cognitive neuroscience ( Schooler, Smallwood, Christoff, Handy, Reichle, & Sayette, 2011 ; Smallwood & Schooler, 2006 , 2015 ). Nevertheless, these definitions have substantial limitations that must be overcome in order for research to move forward. Specifically, the standard definitions do not account for (1) the dynamics of mind wandering, (2) task-unrelated thought that does not qualify as mind-wandering, and (3) the ways that mind-wandering can be task-related.

We then survey three philosophical accounts that improve upon the current psychological definitions in various ways. We first present our account of mind-wandering as “unguided thinking” ( Irving, 2016 ). Next, we review Thomas Metzinger’s (2013) view that mind-wandering can be defined as thought lacking meta-awareness and cognitive agency, as well as Peter Carruthers’s (2015) and Fabian Dorsch’s (2015) definitions of mind-wandering as disunified thinking. We argue that these views are inadequate, and we show that our definition of mind-wandering as unguided thinking not only is conceptually and phenomenologically precise, but also can be operationalized in a principled way for empirical research.

Mind-Wandering as Task-Unrelated Thought or Stimulus-Independent Thought

Experientially, we all know mind-wandering when we see it. On the commute home, a programmer’s thoughts drift away from the sights and sounds of the subway car. At first she imagines the chicken she is brining for dinner. She can almost taste the thyme and rosemary when, suddenly, a line of code pops into her head. She plays with the code for a while, and then, smiling, remembers a joke she heard today . . . and so on. Clearly, the programmer’s mind is wandering. But what exactly makes her train of thought a case of mind-wandering? What precisely is mind-wandering?

Scientists in the empirical literature typically define mind-wandering as thought that is “task-unrelated” or “stimulus-independent,” or both. For example, Smallwood and Schooler define mind-wandering as “a shift in the contents of thought away from an ongoing task and/or from events in the external environment to self-generated thoughts and feelings” ( Smallwood & Schooler, 2015 , p. 488). This definition correctly identifies paradigm cases of mind-wandering. For example, the programmer’s wandering thoughts are unrelated to her ongoing task—commuting home—and to her external environment—the subway car.

Nevertheless, this definition abstracts away from a central feature of mind-wandering, namely, its dynamics ( Christoff, 2012 ; Irving, 2016 ). Wandering trains of thought unfold in a distinctive way over time. Experientially, the thoughts seem to drift freely from one topic (a line of code) to another one (a joke). Irving (2016) notes that the term “mind-wandering” reflects these dynamics: according to the Oxford English Dictionary (online), “to wander” means “to move hither and thither without fixed course or certain aim.” The preceding definition of mind-wandering, however, focuses only on individual mental states and seeks to determine whether their content is related to one’s task or environment. This focus tells us nothing about how trains of thought unfold over time. As we now argue, this definition of mind-wandering in static terms is unsatisfactory in two ways: it cannot differentiate between mind-wandering and other kinds of task-unrelated and stimulus-independent thought; and it cannot account for the fact that mind-wandering can be task-related.

Varieties of Task-Unrelated Thought

Current definitions of mind-wandering cannot distinguish it from depressive rumination, which is typically task-unrelated and stimulus-independent, but which has dynamics that fundamentally differ from that of mind-wandering ( Irving, 2016 ).

Rumination is “a mode of responding to distress that involves repetitively and passively focusing on symptoms of distress and on the possible causes and consequences of these symptoms. . . . People who are ruminating remain fixated on the problems and on their feelings about them” ( Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008 ). Rumination is strongly associated with major depressive disorder, but also is found in the normal population ( Zetsche & Joormann, 2011 ). For example, a non-depressed teacher might ruminate about how to discipline a problem student.

Rumination is frequently task-unrelated and stimulus independent. For example, when a teacher ruminates about a problem student during her commute home, her thoughts are unrelated to her current task (commuting home) and perceptual environment (the subway train). Current researchers, therefore, classify rumination as a form of mind-wandering (e.g., Smallwood & Schooler, 2006 , 2015 ).

Rumination, however, seems antithetical to mind-wandering. Consider the ruminating teacher in contrast to the programmer whose mind is wandering. Both individuals have task-unrelated and stimulus-independent thoughts on their commute home. But the dynamics of their thoughts could hardly contrast more: whereas the teacher’s thoughts fixate on her problem student, the programmer’s thoughts drift from dinner to her computer code to a joke. In general, whereas rumination remains fixed on a single topic, mind-wandering drifts from one topic to the next. One has not wandered—“moved hither and thither”—if one has stayed on a single spot.

Mind-Wandering and Goal-Directed Thought: A Dilemma

Current definitions of mind-wandering face a dilemma concerning the relationship between mind-wandering and cognitive tasks. On the one hand, if we say that all stimulus-independent thinking is mind-wandering, then some mind-wandering will be task-related, because some stimulus-independent thinking is goal-directed. On the other hand, if we say that mind-wandering must be task-unrelated thinking, then we run afoul of empirical evidence that suggests that mind-wandering can be task-related. Let us explain each alternative and its problems in turn.

Suppose we define mind-wandering as any and all stimulus-independent thought. Smallwood and Schooler adopt this view, because they define mind-wandering as “a shift in the contents of thought away from an ongoing task and/ or from events in the external environment ” (2015, p. 488, emphasis added). According to the most restrictive conception in the literature, stimulus-independent mental states not only are non-perceptual states, but also are unrelated to any immediately present perceptual stimuli ( Schooler, Smallwood, Christoff, Handy, Reichle, & Sayette, 2011 ). For example, imagining or thinking about kicking the pigeon in front of you would not count as a stimulus-independent thought, but rather as a stimulus-related thought. Similarly, in a visual detection experiment, thinking “these pictures are flashing by too quickly,” would count as a stimulus-related thought, not a stimulus-independent one. Nevertheless, even this restricted specification of what is required for a thought to be stimulus-independent—that it be a non-perceptual state unrelated to any immediately present perceptual stimuli—classifies much of our goal-directed thought as stimulus-independent and hence (counterintuitively) as mind-wandering.

Consider a mathematician solving a proof in her head or a politician rehearsing a speech under her breath. Both women have thoughts unrelated to their external environments, so they count as mind-wandering, despite their thinking being goal-directed. The problem is that one’s thoughts cannot wander —“move hither and thither without fixed course or certain aim”—if they are directed by a goal. Indeed, theorists at least since Thomas Hobbes (1651) have defined mind-wandering by contrasting it to goal-directed cognition. In one of the first European philosophical discussions of mind-wandering, Hobbes states that thoughts that “wander . . . seem impertinent to each other, as in a Dream” (1651, p. 20). In contrast, he wrote that goal-directed thinking is “more constant; as being regulated by some desire, and designe. For the impression made by such things as wee desire, or feare, is strong and permanent, or, (if it cease for a time,) of quick return” (1651, pp. 20–21).

To distinguish mind-wandering from goal-directed thought, we could maintain that all mind-wandering is task-unrelated thought. According to this conception, neither the mathematician thinking about her proof nor the politician thinking about her speech is mind-wandering, because both are thinking about a task.

But now we face the second horn of the dilemma: Some mind-wandering is task-related ( Irving, 2016 ). Consider our vignette of a programmer whose mind is wandering on her commute home. Her thoughts drift to two personal goals—making dinner and writing code. Empirical evidence indicates that our minds often wander in this way to personal goals ( Klinger, 1999 ). Indeed, one study reported that at least 25% of a person’s wandering thoughts are about a “specific goal (defined as an objective or desired result that an individual endeavors to achieve)” ( Baird, Smallwood, & Schooler, 2011 , p. 1606). Another study found similar results with an experimentally induced goal. Participants were told that they would be quizzed on the names of US states after a “concentration task” ( Morsella, Ben-Zeev, Lanska, & Bargh, 2010 ). When participants had this goal, approximately 70% of their wandering thoughts were about geography (especially US state names). In contrast, the minds of participants in control conditions wandered to geography less than 10% of the time. This finding suggests that goals cause our minds to wander to goal-relevant information.

To see how such findings bear on the current definitions of mind-wandering, we must consider how “task-unrelated” is defined in the scientific literature. Laboratory studies define mind-wandering as thought that is unrelated to the experimental task (e.g., Christoff, Gordon, Smallwood, Smith, & Schooler, 2009 ). So far, so good: thoughts about personal goals such as making dinner are unrelated to the experimental task, and so correctly count as mind-wandering.

In studies of “real-world” mind-wandering outside the lab, however, “tasks” are operationally defined as whatever the person is currently doing . For example, participants are asked whether “my mind had wandered to something other than what I was doing ” ( Kane et al., 2007 , p. 616, emphasis added), or “are you thinking about something other than what you’re currently doing ?” ( Killingsworth & Gilbert, 2010 , p. 932, emphasis added).

Here is the problem. What you are doing often includes working toward the personal goals to which your mind wanders. For example, if we ask you, “what are you doing?” it would be natural for you to answer, “planning dinner” or “preparing for a test.” Therefore, rather than supposing that mind-wandering is task-unrelated thought, we could argue that individuals switch tasks when their minds begin to wander. According to this view, when the programmer’s mind wanders to computer code on the commute home, her task switches to coding from watching for her subway stop. Relative to the new task of coding, her thoughts about code count as task-related.

We can now bring the dilemma into full view. On the one hand, if we say that any and all stimulus-independent thought is mind-wandering, then we muddy the distinction between mind-wandering and goal-directed thinking. On the other hand, if we try to hold onto this distinction by supposing that mind-wandering must be task-unrelated thinking, then we contradict the empirical evidence that shows that task-related mind-wandering is not only possible but frequently actual.

Our diagnosis of the dilemma highlights the dynamics of mind-wandering. The distinction between mind-wandering versus goal-directed thinking does not concern whether mental states are task-unrelated or stimulus-independent. Rather, the distinction concerns how trains of thought unfold over time . When a mathematician solves a problem in her head, she maintains her attention on this problem for a prolonged period of time. In contrast, wandering thoughts “move hither and thither,” drifting between topics unchecked. Because current definitions of mind-wandering abstract away from its dynamics, they cannot distinguish mind-wandering from either rumination or goal-directed thinking. We now propose a theory that overcomes these limitations: mind-wandering is unguided thinking ( Irving, 2016 ).

Mind-Wandering Is Unguided Thinking

We define mind-wandering as unguided thinking. This definition depends on a particular concept of guidance taken from the philosophy of action. Thought or behavior is said to be guided when it is monitored and regulated as it unfolds over time ( Pacherie, 2008 ; Railton, 2006 ). Harry Frankfurt provides a classic philosophical explanation of guidance:

Behavior is purposive when its course is subject to adjustments which compensate for the effects of forces which would otherwise interfere with the course of the behavior. . . . This is merely another way of saying that their course is guided. ( Frankfurt, 1978 , pp. 159–160)

According to this account, “guidance” includes as part of its meaning a counterfactual aspect. To say that behavior is guided implies the following: Were one’s behavior to go off course or deviate from some standard—as a result, for example, of interfering forces—one would alter that behavior in order to bring it back on course. In other words, as Frankfurt states, guidance implies adjusting behavior to compensate for deviations. Thus the concept of guidance also includes a normative aspect: It implies the monitoring and correcting of behavior in relation to some norm or standard. For example, consider conversational interaction. In a conversation, you are guided to maintain a certain distance from your partner, for were your partner to stand too close to you, you would feel discomfited and drawn to step back ( Brownstein & Madva, 2012 ). In other words, your behavior is guided in the sense that it compensates for deviations from the (culturally specific) standards or norms of conversation. It follows that for behavior to be guided, there must be regulatory processes for bringing “deviant” behavior back on track.

We use this technical concept of guidance in order to specify what it means for thought to be guided. We propose that one’s thinking is guided only if one would feel pulled back to its topic, were one distracted from that topic. We also suppose that thinking can be guided in a variety of ways. Our thoughts can be guided back to goal-relevant information, as happens when we are goal-directed, or guided back to affectively salient information, as happens when we ruminate. Although different neurocognitive processes may underlie these two kinds of thinking, we argue that both kinds implement guidance in our technical sense.

Consider goal-directed thinking. In goal-directed thinking, one would feel pulled back to pursuing the goal were one to focus on information that seems irrelevant to it. Imagine a mathematician intently constructing a proof in a busy library. Her attentiveness manifests partly in how her attention is guided back from distractors. Were she to become momentarily distracted by students shuffling their papers, she would likely feel frustrated and pulled back to her work. Thus her mental activity is guided in its being regulated in relation to her goal.

We hypothesize that rumination also is guided. We predict that individuals who break away from their ruminative thoughts will feel pulled or drawn back to them. For thinking to be pulled or drawn back to a particular focus in this way is precisely for it to be counterfactually regulated and thus guided.

Our hypothesis that rumination is guided does not entail that it has the same psychological and neural profile as goal-directed attention (Table 8.1 ). On the contrary, as mentioned earlier, the genus “guided thought” allows for different species of guided thinking that are subserved by different brain processes. For example, top-down cognitive control processes appear to be largely responsible for the guidance of goal-directed thought (e.g., Corbetta & Shulman, 2002 ; Kane & Engle, 2002 ), whereas affective biases of attention and memory ( Todd, Cunningham, Anderson, & Thompson, 2012 ) likely play a strong role in one’s being guided toward ruminative thoughts. Furthermore, goal-directed attention is paradigmatically voluntary, whereas rumination typically is involuntary. The ruminator might complain, “I don’t want to think about distressing thoughts; they just keep pulling me back in.” Nevertheless, we propose that rumination and goal-directed attention are both guided in our technical sense: in either case, if individuals were mentally distracted from their current focus, they would feel their thoughts pulled back to it.

That goal-directed thought and rumination are both guided explains why both kinds of thinking are dynamically stable. Our thoughts remain fixed on a restricted set of information because they are guided to remain there.

In contrast, we define mind-wandering as unguided thinking ( Irving, 2016 ). Whereas a guided thinker would feel pulled back if she were distracted from her current focus, an unguided thinker wanders from one topic (dinner) to another (computer code); her mind drifts unchecked, with nothing to pull her back to a particular focus.

This lack of guidance explains why mind-wandering has an itinerant or unstable dynamics rather than a stable dynamics. Thoughts drift from topic to topic because nothing holds them in place. Thus our definition captures the dynamics of mind-wandering. Moreover, we provide a principled way to distinguish between different varieties of task-unrelated and stimulus-independent thought: in rumination, thoughts are guided to remain on the same topic and hence exhibit greater dynamical stability, whereas in mind-wandering, thoughts are unguided and hence exhibit greater dynamical instability.

Our account avoids the earlier-mentioned dilemma arising from the possibility of task-related mind-wandering. Recall that both wandering thoughts and goal-directed thoughts can be related to everyday tasks, such as planning dinner or writing computer code. Because of this possibility, current definitions of mind-wandering cannot properly distinguish it from goal-directed thinking. According to our account, the difference between them concerns how trains of thought are guided as they unfold over time. Goal-directed thinking is guided to remain on the same topic (e.g., writing code). Mind-wandering is unguided, so it is free to drift from one topic to the next. Its dynamics are unguided even when one’s mind wanders to a personal goal (such as writing computer code). The crucial point is that if one’s thoughts were to drift onward (e.g., to a joke one heard today), one would not be drawn back to a particular focus. 1

Our definition of mind-wandering as unguided thinking overcomes the limitations of previous definitions in the empirical literature. Our definition is based on an account wherein stretches of mind-wandering consist of trains of thought whose dynamics are unguided. This account, however, is not the only account of mind-wandering in the philosophical literature. We will now review two other accounts and critically assess them in relation to our own.

Mind-Wandering as Thought Lacking “Veto Control”

Thomas Metzinger (2013 ; see also Metzinger, Chapter 9 in this volume) proposes a theory of mind-wandering that helps to explain the relationship between mind-wandering and cases of goal-directed thinking, such as a mathematician constructing a proof. 2 Metzinger allows that mind-wandering can be goal-directed, and so his theory can accommodate the evidence that our minds frequently wander to our personal goals. Nevertheless, he maintains that mind-wandering differs from fully “autonomous” forms of goal-directed thinking, such as a mathematician consciously constructing a proof. In Metzinger’s view, goal-directed thinking is “mentally autonomous” only if one has the kind of cognitive control over one’s thoughts that he calls “veto control.”

The concept of “veto control” comes from cognitive science. It refers to the person’s ability to “withhold a . . . [behavior] 3 whose preparation and path towards execution has already begun” ( Filevich, Kühn, & Haggard, 2012 , p. 1108). Consider the following example in which you exercise veto control:

You are posting a letter, and are just about to release your grip on it and let it fall into the post box, when you suddenly get the feeling that you should check whether you put a stamp on the envelope. You tighten your grip and inspect the letter. ( Filevich et al., 2012 , p. 1108)

Note that you would have possessed veto control even if you had released the letter, because veto control requires only that you are able––and know that you are able––to suspend the relevant behavior ( Metzinger, 2013 , p. 4).

Metzinger argues that when our minds wander, we lack veto control over our thoughts. Thus he distinguishes mind-wandering from autonomous goal-directed thinking that we can suspend at will—for example, consciously constructing a math proof. In support of this view, Metzinger appeals to evidence that mind-wandering unfolds without meta-awareness ( Schooler et al., 2011 ). 4 “Meta-awareness” is defined as one’s explicit knowledge of the current contents of thought or one’s current conscious state ( Schooler, Smallwood, Christoff, Handy, Reichle, & Sayette, 2011 ). Thus meta-aware mental states are higher-order mental states that are about one’s ongoing or just past mental states. One example is a lucid dreamer’s meta-awareness that she is dreaming (see Windt and Voss, Chapter 29 in this volume). Another example is the sudden realization that your mind was wandering.

Metzinger’s argument has two premises. First, meta-awareness is necessary for veto control over a mental state or process ( Metzinger, 2013 , p. 3): A person cannot knowingly terminate something of which she is unaware. (Suppose I discover that you were not paying attention and I ask, “Why didn’t you stop your mind from wandering earlier?” You might reasonably respond, “I didn’t know my mind was wandering until just now.”) Second, Metzinger contends that whenever a person’s mind is wandering, she lacks meta-awareness of her wandering thoughts. From these two premises, it follows that people lack veto control over their wandering thoughts. Thus, Metzinger’s account suggests that mind-wandering can be defined as thinking that lacks meta-awareness and veto control.

The problem with this account is that the second premise—that mind-wandering always occurs without meta-awareness—is questionable. The evidence suggests that although mind-wandering sometimes occurs without meta-awareness, this is not always the case ( Christoff et al., 2009 ; Schooler, Smallwood, Christoff, Handy, Reichle, & Sayette, 2011 ; Smallwood & Schooler, 2006 ). Many studies of mind-wandering use self-reports to assess meta-awareness. Individuals who catch themselves mind-wandering or who report that their minds were wandering upon being probed are asked whether they were previously aware that their mind was wandering. For example, Smallwood and colleagues gave participants the following instructions in order to distinguish between aware (“tuning out”) versus unaware (“zoning out”) mind-wandering:

Tuning Out : Sometimes when your mind wanders, you are aware that your mind has drifted, but for whatever reason you still continue to read. This is what we refer to as “tuning out”––i.e., when your mind wanders and you know it all along. Zoning Out : Other times when your mind wanders, you don’t realize that your thoughts have drifted away from the text until you catch yourself. This is what we refer to as “zoning out”––i.e., when your mind wanders, but you don’t realize this until you catch it. ( Smallwood, McSpadden, & Schooler, 2007 , p. 533)

Across all conditions, Smallwood and colleagues found that tuning out occurred as frequently or more frequently than zoning out. Therefore, it may be that mind-wandering occurs at least as often with meta-awareness as without it (cf. Smallwood et al., 2004 ; Smallwood, Beach, Schooler, & Handy, 2008 ).

Metzinger argues that cases of apparently autonomous mind-wandering involve the mere “illusion of control” ( Metzinger, 2013 ; cf. Schooler et al., 2011 ), so he might question the reliability of reports of “tuning out” (mind-wandering with awareness). Nevertheless, tuning out and zoning out have different behavioral and neural profiles ( Schooler et al., 2011 ). For example, compared to tuning out, zoning out is associated with better reading comprehension ( Smallwood et al. 2008 ) and more activation of default network and executive regions ( Christoff et al. 2009 ) that are generally associated with mind-wandering ( Fox et al. 2015 ). It is not clear how to explain these differences, if reports of tuning out are entirely illusory.

Another limitation of Metzinger’s theory is that it neglects the dynamics of mind-wandering. Veto control and the presence versus absence of meta-awareness have no essential connection to how one’s thoughts unfold over time, according to his account. Therefore, his account cannot distinguish mind-wandering from rumination. Ruminators often seem to lack meta-awareness and hence veto control over their thoughts. For example, a commuter might fixate on her problems and distress without realizing that she has stopped watching for her subway stop. Because she is unaware that she has begun to ruminate, she cannot disengage from (veto) her distressing thoughts and bring herself back on task. Indeed, trait ruminators show impaired disengagement across a range of tasks ( Whitmer & Gotlib, 2013 ). This finding suggests that rumination frequently unfolds without veto control. Metzinger’s theory does not have the resources to explain how mind-wandering differs from this antithetical phenomenon of rumination.

Our account of mind-wandering as unguided thinking therefore has two advantages over Metzinger’s account ( Irving, 2016 , pp. 567–568). First, we allow that mind-wandering can unfold with or without meta-awareness. During cases of tuning out—“when your mind wanders and you know it all along” ( Smallwood et al., 2007 , p. 533)—we propose that you have meta-awareness of and thus veto control over your stream of unguided thoughts. Second, our account captures the dynamics of mind-wandering. Accordingly, we can explain how rumination and mind-wandering differ: Whereas the former is guided, the latter is not.

Mind-Wandering as Disunified Thinking

Peter Carruthers (2015) and Fabian Dorsch (2015) independently have proposed accounts of mind-wandering that rival the explanatory power of our own account. We focus on Carruthers’s theory, but our critical discussion applies to both philosophers. Carruthers discusses mind-wandering because it provides an apparent counterexample to his view that all thinking is active and goal-directed. He concedes that mind-wandering does “not seem, introspectively, to be active in nature. Sometimes one’s thoughts change direction for no apparent reason (especially when one’s mind is wandering)” ( Carruthers, 2015 , p. 166). Therefore, he must explain away the apparent difference between mind-wandering and goal-directed thought.

Carruthers explains away this apparent difference by drawing an analogy between mind-wandering and wandering around a garden: “Mind wandering is active, I suggest, in much the same sense that someone physically wandering around in a garden is active” ( Carruthers, 2015 , pp. 167–168). Dorsch (2015) draws a similar analogy between mind-wandering and physically wandering around a city. Both philosophers maintain that short stretches of physical and mental wandering are active. As you wander around a garden you might actively smell a rose or wish upon a dandelion. Similarly, you might actively plan dinner or write code while your mind wanders. Nevertheless, longer stretches of physical and mental wandering seem passive because no overarching goal unifies your thoughts. Given this point, Carruthers and Dorsch can explain away the apparent difference between mind-wandering and paradigm cases of goal-directed thought, such as a mathematician solving a proof in her head. Whereas the mathematician’s thoughts are all unified under a single goal (solving the proof), the mind-wanderer’s thoughts concern many goals (planning dinner, writing code, and so on). Thus mind-wandering seems more passive than goal-directed thought, though both are active when we look at them closely enough.

Carruthers’s and Dorsch’s discussions suggest that mind-wandering be defined as disunified thinking. A sequence of thoughts constitutes mind-wandering if and only if those thoughts are not unified under a common goal. This definition has major advantages. First, it captures the dynamics of mind-wandering: by definition, our wandering thoughts are dynamically unstable in the sense that they are not unified under a common goal. Second, this definition can account for the puzzling relationship between mind-wandering and goal-directed thought. On the one hand, short stretches of mind-wandering are related to tasks (such as preparing for a quiz), as the empirical evidence suggests. On the other hand, mind-wandering contrasts with goal-directed thinking because it is disunified.

Despite the advantages of this conception of mind-wandering, it has a problematic consequence, which we can bring out in the following example. Imagine someone who works for 10 minutes composing part of a lecture, then opens his web browser and responds to some emails for 6 minutes, and then looks outside the window, studying the pigeons across the street for 90 seconds. Furthermore, suppose that the person attentively pursues each goal. Nevertheless, no overarching goal unifies this whole sequence of thoughts, so they count as mind-wandering. Shifting from goal to goal in this way seems commonplace. Therefore, if we define mind-wandering as disunified thinking, most trains of goal-directed thinking will count as mind-wandering. But then it seems that Carruthers and Dorsch have not captured the difference between mind-wandering and goal-directed thinking at all.

A deeper problem lurks in the vicinity. Whether thinking counts as disunified, and thus as mind-wandering, depends on the scale of observation or how far we zoom out (Figure 8.1 ). Suppose we examine the person’s thoughts in the previous example. In the first five minutes, his attention is wholly guided by the goal of composing his lecture. During that interval, his attention is unified and his mind is not wandering. But if we zoom out to a seventeen-minute interval, we find thoughts about three separate goals—composing a lecture, writing emails, and watching pigeons. From this broader perspective, his attention is disunified and his mind is wandering. The problem is that we lack principled reasons for deciding how far to zoom out, and therefore we lack principled reasons for saying whether his mind is wandering at any given point in time.

Disunity and zoom.

This consequence undermines the scientific methods we use to study mind-wandering. These methods require that we be able to specify when the mind is wandering versus when it is not, so that we can study the distinctive features of MIND-WANDERING (such as ITS contents and neural correlates) versus other kinds of THINKING. For example, Christoff et al. (2009) compared neural activation when individuals were concentrating on a task versus mentally wandering away from it. If we define mind-wandering as disunified thinking, then we cannot use these methods, because if we zoom out, then the on-task thoughts are probably going to count as wandering thoughts. No methodological innovation could solve this problem. In other words, given the definition of mind-wandering as disunified thinking, there will be no principled way to distinguish mind-wandering from goal-directed thought. Therefore, this definition is a non-starter for the cognitive science of mind-wandering.

In contrast, our definition of mind-wandering as unguided thought does not face these problems. We provide a principled way to distinguish goal-directed and wandering thought: The former is guided; the latter is not. Therefore, our definition is preferable on conceptual grounds as well as being more amenable to empirical investigation.

Psychologists, cognitive neuroscientists, and philosophers should be partners in the scientific investigation of mind-wandering. The challenges facing this young field are not only empirical, but also conceptual and theoretical. Our chapter begins with a philosophical critique of the most widely accepted definitions of mind-wandering in cognitive psychology. This critique stems from the idea that mind-wandering is fundamentally dynamic. Our definition uses the technical philosophical notion of “guidance” to capture its dynamics. Compared to the other extant philosophical definitions, our definition of mind-wandering as unguided thought is not only more theoretically defensible, but also more scientifically tractable. Putting this definition to work in cognitive science will require close collaborations with psychologists and cognitive neuroscientists. For example, difficult questions remain about how to measure the dynamics of mind-wandering ( Christoff, 2012 ) and how to relate the philosophical notion of guidance to dynamical neural networks and psychological processes ( Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016 ). The path forward requires that psychologists, cognitive neuroscientists, and philosophers work together to advance our understanding of mind-wandering.

One might worry that our view characterizes mind-wandering as too disordered . Although mind-wandering is certainly less stable than goal-directed or ruminative thought, our wandering thoughts are not entirely random: for example, our minds often wander to personal goals and concerns (as noted earlier) and between associated thoughts. For similar reasons, we elsewhere propose a neuroscientific model on which the dynamics of mind-wandering are somewhat constrained, albeit less so than goal-directed or ruminative thoughts ( Christoff, Irving, et al., 2016 ). Fortunately, our philosophical model of mind-wandering is compatible with the presence of dynamic constraints on mind-wandering. This is because guidance is not the only way that thought can be constrained. Mind-wandering can be probabilistically constrained, in that we often think of particular things (e.g., close associations, personal goals and concerns). Yet we contend that when the mind wanders, no guidance mechanism holds our thoughts in place; when the mind wanders to unusual ideas, or from one topic to another, nothing pulls us back. See Irving (2016) for an in-depth discussion of the different types of constraints on thought, including those that are present and absent during mind-wandering.

Much of the material for this section is adapted from Irving (2016) .

Filevich and colleagues originally defined “veto control” as the ability to “withhold an action.” We have changed the definition, replacing “action” with “behavior,” because veto control arguably is necessary for action (as opposed to mere movement). In that case, defining “veto control” as the ability to withhold an action would trivially imply that one never lacks veto control.

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Reconceptualizing mind wandering from a switching perspective

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• Published: 29 March 2022
• Volume 87 , pages 357–372, ( 2023 )

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• Yi-Sheng Wong   ORCID: orcid.org/0000-0002-5752-4679 1 , 2 , 3 ,
• Adrian R. Willoughby   ORCID: orcid.org/0000-0002-4214-2635 3 , 4 &
• Liana Machado   ORCID: orcid.org/0000-0002-0856-3831 1 , 2  

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Mind wandering is a universal phenomenon in which our attention shifts away from the task at hand toward task - unrelated thoughts. Despite it inherently involving a shift in mental set, little is known about the role of cognitive flexibility in mind wandering. In this article we consider the potential of cognitive flexibility as a mechanism for mediating and/or regulating the occurrence of mind wandering. Our review begins with a brief introduction to the prominent theories of mind wandering—the executive failure hypothesis, the decoupling hypothesis, the process - occurrence framework, and the resource - control account of sustained attention. Then, after discussing their respective merits and weaknesses, we put forward a new perspective of mind wandering focused on cognitive flexibility, which provides an account more in line with the data to date, including why older populations experience a reduction in mind wandering. After summarizing initial evidence prompting this new perspective, drawn from several mind - wandering and task - switching studies, we recommend avenues for future research aimed at further understanding the importance of cognitive flexibility in mind wandering.

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Introduction

In the past two decades, there has been growing interest in understanding the basic psychological processes of mind wandering and its underlying mechanisms (for a review, see Kvavilashvili & Rummel, 2020 ). Mind wandering refers to a phenomenon in which our attention shifts away from the task at hand toward task - unrelated thoughts (for reviews, see Smallwood et al., 2018 ; Smallwood & Schooler, 2006 , 2015 ). It has been estimated that up to 50% of our waking time is spent mind wandering (Kane et al., 2007 ; Killingsworth & Gilbert, 2010 ). Despite its prevalence, most people view mind wandering from a negative perspective, in which our performance will drop if our mind wanders (for reviews, see Mooneyham & Schooler, 2013 ; Stan & Christoff, 2018 ). Indeed, a number of studies have found a negative association between mind wandering and primary task performance, including poorer performance in daily functioning (McVay et al., 2009 ) and driving (Baldwin et al., 2017 ; Yanko & Spalek, 2014 ). However, studies have also shown a positive relationship between mind wandering and both mood and cognition (e.g., Gable et al., 2019 ; Mazzoni, 2019 ; Welz et al., 2018 ). In order to understand how to minimize the costs of mind wandering and maximize its benefits, it is therefore important to determine what factors regulate its occurrence.

Despite mind wandering inherently involving a shift in mental set, no existing study to our knowledge has explicitly examined the role of cognitive flexibility in mind wandering. In the present article, we consider the potential of cognitive flexibility to help explain the nature of mind wandering and its tendencies. In an effort to advance the field, here we first briefly review and discuss the most prominent theories of mind wandering—the executive failure hypothesis (McVay & Kane, 2009 , 2010 , 2012b ), the decoupling hypothesis (Smallwood & Schooler, 2006 ), the process - occurrence framework (Smallwood, 2013 ), and the resource - control account of sustained attention (Thomson et al., 2015 ). Then, we put forward a new perspective centered around cognitive flexibility that was prompted by findings from several mind - wandering studies in older adults (e.g., Gyurkovics et al., 2018 ; Jordão et al., 2019 ; Niedzwienska & Kvavilashvili, 2018 ) and mind - wandering studies involving task-switching paradigms in young adults (e.g., Arnau et al., 2020 ; Kam & Handy, 2014 ; Thomson et al., 2014 ). According to this new switching perspective, the reason why some populations (e.g., healthy older adults) experience distinct patterns of mind wandering stems from differences in cognitive flexibility, as instances of mind wandering are in fact instances of mental set shifting (see Murray & Krasich, 2020 , for a similar argument). After presenting the evidence supporting this new perspective, we put forward recommendations for future research aimed at further understanding the importance of cognitive flexibility in mind wandering.

Existing theories of mind wandering

Executive failure hypothesis.

According to the executive failure hypothesis (McVay & Kane, 2009 , 2010 , 2012b ), the occurrence of mind wandering represents a failure in the control of executive resources to keep attention on the current task, as the suppression of mind wandering requires executive control. One key form of executive control is working memory. This hypothesis posits that individuals with lower working memory capacity (i.e., those who are less able to hold information in an active, quickly retrievable state; Engle, 2002 ) are less capable of maintaining task focus over extended periods of time and keeping mind wandering at bay, and consequently experience more mind wandering. In support of this hypothesis, studies have found that individuals with lower working memory capacity have higher self - reported mind - wandering rates than individuals with higher working memory capacity (McVay & Kane, 2009 , 2012a , 2012b ; Robison & Unsworth, 2018 ; Unsworth & Robison, 2020 ), and working memory capacity can reliably predict how often one’s mind wanders (Kane et al., 2001 ; Robison & Unsworth, 2018 ). A meta - analysis that examined the association between mind wandering, executive resources (e.g., working memory capacity), and task performance also provided support for this hypothesis, by showing that individuals with lower working memory capacity tend to engage in more mind wandering than individuals with higher working memory capacity (Randall et al., 2014 ).

Additional evidence in support of the executive failure hypothesis comes from research on mind wandering involving individuals with attention - deficit/hyperactivity disorder (ADHD; for a review, see Bozhilova et al., 2018 )—a neurodevelopmental disorder characterized by inattentiveness, hyperactivity, and/or impulsivity (American Psychiatric Association, 2013 ). Using a probe - caught method, in which participants are intermittently interrupted during a vigilance task and probed to report where their attention is focused, Shaw & Giambra, ( 1993 ) showed that participants with a childhood history of ADHD diagnosis reported experiencing more task - unrelated thoughts during task performance than participants with no history of ADHD. Another study, which distinguished between deliberate mind wandering and spontaneous mind wandering, found that the occurrence of spontaneous, but not deliberate, mind wandering is positively associated with ADHD symptom severity (Seli et al., 2015 ). A similar result was obtained by Franklin et al., ( 2017 ), who found that a composite index of ADHD symptoms was positively correlated with both the frequency of mind wandering and a lack of awareness of mind wandering. More recently, Mowlem et al., ( 2019 ) demonstrated that elevated frequencies of mind wandering in adults with ADHD were positively correlated with self - reported measures of functional impairment across major life domains (e.g., school), and that the contribution of mind wandering to their impairment was independent of the core ADHD symptoms (inattention, hyperactivity, and impulsivity). Given that most individuals with ADHD have deficits in a variety of cognitive domains (e.g., Coutinho et al., 2018 ; Kasper et al., 2012 ; Ramos et al., 2020 ), these studies suggest that the excessive mind wandering they experience could be attributable to, at least in part, a failure of executive control (McVay & Kane, 2010 ).

Decoupling hypothesis

The decoupling hypothesis (Smallwood & Schooler, 2006 ) suggests that decreased performance during mind wandering occurs primarily because our attention has become decoupled from the task at hand and is instead coupled to task - unrelated thoughts. This decoupling process is important as it prevents information processing of extraneous stimuli from interfering with our current mental focus (Smallwood et al., 2011 ) in order to ensure continuity of the train of thought (Smallwood, 2013 ). In other words, the decoupling hypothesis proposes that mind wandering is a process that relies on some of the same cognitive mechanisms involved in maintaining focused attention on the task at hand and thus directly competes with primary task performance for executive resources (Smallwood & Schooler, 2006 ).

Several event - related potential (ERP) studies have provided strong evidence for this hypothesis. For example, using the sustained attention to response task (SART; Robertson et al., 1997 ), Smallwood et al., ( 2008 ) showed that participants had a reduced P300 amplitude during self - reported mind wandering relative to on - task episodes. The P300 is a positive potential that peaks around 300 ms after stimulus presentation and is believed to reflect the extent to which the stimulus representation is updated in working memory (Donchin, 1981 ; Donchin & Coles, 1988 ) and/or the amount of executive resources allocated toward the stimulus (Kramer & Strayer, 1988 ; Wickens et al., 1983 ), with higher amplitude indicating more revision of the representations and/or more executive resources directed to processing the stimulus (for reviews, see Polich, 2007 ; Verleger, 2020 ). Because the P300 can provide an index of executive resources (e.g., Kramer & Strayer, 1988 ; Wickens et al., 1983 ), the decreased P300 amplitude during mind wandering indicates that our executive resources have been withdrawn, at least partly, from the primary task and are presumably directed toward task - unrelated thoughts (Smallwood, 2010 ; Smallwood & Schooler, 2006 ). Similar results were obtained in subsequent studies (Baldwin et al., 2017 ; Barron et al., 2011 ; Kam et al., 2014 ; Maillet et al., 2020 ).

Process-occurrence framework

The process - occurrence framework, which was proposed by Smallwood, ( 2013 ), emphasizes the necessity of distinguishing between the onset of mind wandering and the continuation of the mind-wandering episode, linking McVay and Kane’s view (i.e., executive control failure) to the onset and Smallwood and Schooler’s view (i.e., mind wandering requires executive resources) to the continuation of the episode. According to this framework, under tasks requiring sustained attention, executive control can keep mind wandering at bay by ensuring the continuity of the train of task - related thought. However, when mind wandering occurs (e.g., due to executive control failure; McVay & Kane, 2009 , 2010 , 2012b ), executive control shifts away (i.e., decouples) from the task at hand to enable the continuation of the mind-wandering episode (which consumes the same executive resources as the task at hand; Smallwood & Schooler, 2006 ), leaving insufficient executive resources for the primary task, thereby resulting in impaired task performance (Smallwood et al., 2012 ).

As proposed by Smallwood, ( 2013 ), there are at least two reasons why one would mind wander more. First, because the individual has difficulties in ensuring the continuity of their train of thought (see also McVay & Kane, 2010 ). This account could explain why individuals with ADHD tend to experience more mind wandering episodes (Bozhilova et al., 2018 ; Franklin et al., 2017 ; Mowlem et al., 2019 ; Seli et al., 2015 ; Shaw & Giambra, 1993 ). Second, because the individual considers their currently relevant personal concerns or unresolved goals (e.g., submit the assignment before the end of the day) as having higher priority than the demands of the task at hand, and thus shifts their attention toward these concerns (see also Klinger, 1975 , 1999 ). This account could explain why older adults report less mind wandering than young adults (e.g., Jordão et al., 2019 ; Maillet & Schacter, 2016 ), as they tend to report having fewer concerns (Parks et al., 1989 ). In the former case, according to this framework (Smallwood, 2013 ), the individual should experience more frequent mind-wandering episodes, whereas in the latter case, the individual should experience longer episodes of mind wandering.

Although it is difficult to identify different states and processes involved in mind wandering, primarily because people normally do not realize when they first start mind wandering but only notice some time later (Smallwood, 2013 ; Zukosky & Wang, 2021 ), there has been one study to date that made an attempt at this (Voss et al., 2018 ). In this study, the researchers characterized the mind-wandering process by combining the self-caught and the probe-caught methods to estimate the duration of focus (defined as the time period from when the person first started focusing on the task at hand to the moment that mind wandering began), which was taken as a measure of one’s ability to maintain task focus and resist the occurrence of mind wandering, and the duration of mind wandering (defined as the time period from when the mind-wandering episode began to the moment that the individual caught themselves and reported it by pressing a button), which was taken as a measure of processes that keep one in the mind-wandering state. The researchers then investigated the association of these measures with working memory capacity. The results showed a strong positive correlation between the duration of focus and working memory capacity, which is consistent with previous findings that individuals with higher working memory capacity could maintain task focus over longer periods of time (Kane et al., 2001 ; McVay & Kane, 2009 , 2012a , 2012b ; Randall et al., 2014 ; Robison & Unsworth, 2018 ; Unsworth & Robison, 2020 ). However, no relationship was observed between the duration of mind wandering and working memory capacity, indicating that one’s tendency to engage in and detect the mind-wandering state was not affected by working memory capacity. The Voss et al., ( 2018 ) study, therefore, provides initial evidence to support the process - occurrence framework (Smallwood, 2013 ) that there are at least two distinct states and processes involved in mind wandering.

Resource-control account of sustained attention

According to the resource - control account of sustained attention (Thomson et al., 2015 ), mind-wandering state is the default mental state and thus there is a continuous bias for executive resources to be directed toward mind wandering (see also Baird et al., 2011 ; Smallwood, 2013 ; Smallwood et al., 2009 ). This theory posits that the occurrence of mind wandering should be associated with decreases in motivation and/or effort to keep attention on the task at hand over time. In other words, as time-on-task increases, executive resources are less likely to be allocated to the task at hand and are more likely to be allocated to mind wandering, leaving insufficient executive resources for the primary task and thereby resulting in impaired task performance. In support of this theory, studies have found a negative association between time on task and primary task performance, and a positive association between time on task and rates of self - reported mind wandering (Brosowsky et al., 2020 ; Krimsky et al., 2017 ; McVay & Kane, 2012a ; Thomson et al., 2014 ).

Interim summary

Taken together, these four theories suggest that mind wandering is a state of decoupled information processing (Smallwood and Schooler’s view) that involves at least two component processes (Smallwood’s view): the initiation of mind wandering, which can be attributed to a failure of executive control (McVay and Kane’s view), and the continuation of the mind-wandering episode, which is a resource-dependent process (Smallwood and Schooler’s and Thomson et al.’s view). Although these four theories significantly advance our understanding of mind wandering, they are not without weaknesses or alternative interpretations. In the next section, we discuss these and put forward a new perspective of mind wandering focused on cognitive flexibility, which offers novel insight that aids towards our general understanding of mind wandering.

Insight from a switching perspective

Cognitive flexibility, which can also be referred to as mental set shifting or switching, is one of the three core executive control functions (along with inhibitory control and working memory) that enables us to adjust our thoughts and actions in response to changed priorities or demands (Buttelmann & Karbach, 2017 ; Diamond, 2013 ; Miyake et al., 2000 ). To adapt to changing priorities, for example, we need to inhibit previously relevant thoughts and actions and activate newly relevant thoughts and actions in working memory. In this way, mental set shifting requires involvement of both inhibitory control and working memory (Diamond, 2013 ). With regard to mind wandering, we propose that it requires cognitive flexibility, as the occurrence of mind wandering entails inhibition of one’s primary task mental set (which enables decoupling to occur) and activation of task - unrelated thoughts in working memory (see Fig.  1 ).

Conceptual framework for viewing mind wandering from a switching perspective. Maintenance of both primary task and mind - wandering mental sets occur in working memory. Each change of mental set requires inhibition of the previously relevant mental set. Grey area represents the time in which primary task performance costs arise

Considering this switching view alongside existing frameworks and models such as the metacontrol state model (Hommel, 2015 ), which describes the balance between flexibility and persistence in cognitive processing (Zhang et al., 2020 ), may provide a way to understand variability in mind-wandering frequency. For instance, given that ADHD has long been found to be associated with dysregulated dopamine neurotransmission (Cook et al., 1995 ), and dopamine-related interindividual differences have been hypothesized to be associated with interindividual variability in metacontrol defaults (i.e., the control of the current cognitive-control settings; Hommel & Colzato, 2017 ), it seems possible that the increased mind wandering experienced by individuals with ADHD might be associated with a default metacontrol setting biased towards flexibility (i.e., weak goal shielding and weak mutual inhibition of task-related and task-unrelated mental sets) that may be related to imbalances of neurotransmitters. Moreover, given that older adults tend to report higher levels of positive affect (e.g., Frank et al., 2015 ) and motivation (e.g., Nicosia & Balota, 2021 ; Ryan & Campbell, 2021 ; Seli et al., 2021 ) than young adults during task performance, and situational factors such as these have been hypothesized to induce a metacontrol setting biased towards persistence (Hommel & Colzato, 2017 ), it seems possible that older adults’ less frequent reports of mind wandering (e.g., Arnicane et al., 2021 ; Jordão et al., 2019 ; Maillet & Schacter, 2016 ) might be associated with a stronger bias towards persistence (i.e., stronger goal shielding and stronger mutual inhibition of task-related and task-unrelated mental sets).

It may also be possible to account for the effect of mind wandering on creativity (e.g., Gable et al., 2019 ; Murray et al., 2021 ; Steindorf et al., 2021 ; Yamaoka & Yukawa, 2020 ) by integrating the switching perspective of mind wandering with both the metacontrol state model (Hommel, 2015 ) and the dynamic framework of thought (Christoff et al., 2016 )—a model that provides insight into how thoughts that focus on personally or affectively salient information (i.e., automatic constraints) and thoughts that focus on goal-related information (i.e., deliberate constraints) dynamically influence the nature of thought over time. For example, given that both of these schemas emphasize the importance of flexibility or shifting between mental states in idea generation (for reviews, see Girn et al., 2020 ; Zhang et al., 2020 ), it seems possible that the relationship between mind wandering and creative thinking might be mediated by cognitive flexibility. This speculation may be worthy of future research.

In short, we believe that viewing mind wandering from a switching perspective may help explain, at least in part, why some populations experience more mind-wandering episodes while others experience fewer episodes, why participants who indicate higher levels of motivation are less likely to engage in mind wandering during task performance, and why mind wandering is sometimes linked to enhanced creativity.

Limitations and an alternative viewpoint for the executive failure hypothesis

Although the executive failure hypothesis (McVay & Kane, 2009 , 2010 , 2012b ), which posits that working memory plays a critical role in keeping mind wandering at bay, could explain the higher levels of mind wandering in healthy young adults with lower working memory capacity and individuals with ADHD, mind-wandering research involving other cohorts with lower working memory capacity has yielded results that challenge this account. For instance, a meta - analysis of 21 studies investigating aging effects in mind wandering revealed that older adults tend to report fewer instances of mind wandering when engaged in cognitive tasks (Jordão et al., 2019 ). This is puzzling given that according to the executive failure hypothesis, one would expect the rates of mind wandering to increase—not decrease—in older adults (for a review, see Maillet & Schacter, 2016 ), as executive control functions generally decline with advancing age (e.g., Craik & Salthouse, 2011 ; Foster et al., 2007 ; Machado, 2021 ). Furthermore, using the SART, Gyurkovics et al. ( 2018 ) found that individuals with early-stage Alzheimer’s disease reported experiencing fewer task - unrelated thoughts and more task - related thoughts than healthy age - matched controls, again indicating reduced incidence of mind wandering despite individuals with Alzheimer's disease showing declines in executive functioning (Guarino et al., 2018 ). Similar results have been reported in studies involving individuals with Parkinson’s disease (Walpola et al., 2020 ), who are also known to suffer from executive dysfunction (Flannery et al., 2018 ; McKinlay et al., 2010 ; Ramos & Machado, 2021 ), and individuals with amnestic mild cognitive impairment (Niedzwienska & Kvavilashvili, 2018 ).

A counterargument to the claim that lower levels of mind wandering in these older populations stand against the executive failure hypothesis stems from the fact that mind - wandering studies mostly rely on self - report measures. In relation to this, some researchers have attributed the finding of reduced mind wandering in healthy and cognitively impaired older adults to a lack of validity of their mind - wandering reports (Gyurkovics et al., 2018 ; Jackson & Balota, 2012 ; Zavagnin et al., 2014 ). However, several studies have demonstrated that these populations’ self - reported mind-wandering data are as valid as those by controls, by demonstrating that during self - reported off - task episodes, the two groups exhibited similar levels of disrupted task performance (e.g., Arnicane et al., 2021 ; McVay et al., 2013 ; Niedzwienska & Kvavilashvili, 2018 ). Moreover, eye movement (Frank et al., 2015 ) and brain activation (Maillet & Rajah, 2016 ; Walpola et al., 2020 ) patterns reliably predicted self - reported mind - wandering episodes in older adults and individuals with Parkinson’s disease, indicating that they were able to report their mind - wandering episodes accurately. These results, therefore, suggest that the explanation that decreased mind wandering relates to lack of validity of mind-wandering measures in these older populations does not hold up.

To shed light on decreased mind wandering in older cohorts, here we offer an alternative account of mind wandering focused on cognitive flexibility (for a summary of other alternative explanations for age-related declines in mind wandering, see Seli, Maillet, et al., 2017 ; Seli, Ralph, et al., 2017 ). According to this account, the reduced mind - wandering frequency seen in healthy older adults and those with Alzheimer’s disease, Parkinson’s disease, and amnestic mild cognitive impairment could be attributable, at least partly, to declines in cognitive flexibility (e.g., a reduced ability to switch between task - related and task - unrelated thoughts; see Fig.  2 ). This line of reasoning fits well with research showing that these older populations generally have a reduced capability to activate (e.g., to initiate a switch of mental set) and maintain cognitive representations (e.g., to maintain the new mental set; Craik & Salthouse, 2011 ; Lindenberger & Mayr, 2014 ; Traykov et al., 2007 ), and exhibit longer response times and/or higher error rates on switch trials relative to repetition trials (e.g., Brett & Machado, 2017 ; Rey-Mermet & Gade, 2018 ; Wasylyshyn et al., 2011 ).

Summary of results gathered from several mind-wandering, working memory, and task-switching studies . AD Alzheimer’s disease, PD Parkinson’s disease, aMCI amnestic mild cognitive impairment, WMC working memory capacity. Red circle represents poorer performance and green circle represents better performance, relative to the comparison group. White thought bubble cloud represents mind-wandering frequency, with fewer clouds representing less frequent occurrences of mind wandering, relative to the comparison group

Furthermore, given that previous research in healthy young adults has revealed a negative association between working memory capacity and mind - wandering frequency (McVay & Kane, 2009 , 2012b ; Robison & Unsworth, 2018 ; Unsworth & Robison, 2020 ), and a negative correlation between working memory capacity and task - switching performance (Miyake et al., 2000 ; Oberauer et al., 2003 ; Shipstead et al., 2015 ; for more details, see Draheim et al., 2016 ), it seems plausible that healthy young adults with lower working memory capacity might be more capable of adjusting their executive resources to different mental sets (including task-unrelated mental sets) due to their superior switching abilities (see the “ Future directions ” for further elaboration of this conjecture; see Fig.  2 ). In short, although the executive failure hypothesis (McVay & Kane, 2009 , 2010 , 2012b ) holds up quite well for younger populations, the switching account of mind wandering put forward here could potentially also explain the patterns of results in these populations. Moreover, in contrast to the executive failure hypothesis which cannot account for the lower levels of mind wandering observed in older populations (as it predicts that these populations should exhibit elevated levels of mind wandering due to having lower working memory capacity), our switching account of mind wandering fits well with the existing data, suggesting that cognitive flexibility may play a more important role than working memory does in mediating and/or regulating the occurrence of mind wandering.

An alternative interpretation for the decoupling hypothesis

Although studies using ERPs have provided evidence in favor of the decoupling hypothesis (Smallwood & Schooler, 2006 ) by demonstrating that mind wandering reduces the cortical processing of the task at hand (as reflected by the reduced P300 amplitude during mind wandering; Baldwin et al., 2017 ; Barron et al., 2011 ; Kam et al., 2014 ; Maillet et al., 2020 ; Smallwood et al., 2008 ), these findings could also be interpreted from a switching perspective. Previous research on task switching has consistently revealed a reduced P300 amplitude on switch trials relative to repetition trials (e.g., Barcelo et al., 2002 ; Hsieh & Cheng, 2006 ; Kieffaber & Hetrick, 2005 ; Mueller et al., 2007 ; Poljac & Yeung, 2014 ; Vandamme et al., 2010 ; Wylie et al., 2003 ). According to Jost et al. ( 2008 ), the reduced P300 amplitude in switch trials is thought to indicate that context updating (i.e., the comparison of the attributes of an incoming stimulus with an internal representation and the subsequent updating of the internal representation; Donchin, 1981 ; Donchin & Coles, 1988 ) is less easily achieved. Another explanation comes from Wylie et al., ( 2003 ), who suggested that the reduced P300 amplitude reflects a competition between task sets or rules, with the idea being that on switch trials the competition between task - specific response sets or rules is greater. This results in both a reduction in P300 amplitude and an increase in response time and/or error rate because the activation of the currently relevant task representation is less enhanced. Using the findings from these task - switching studies as a foundation, we posit that the attenuated P300 amplitude during mind wandering could reflect two possible processes, including ( a ) less efficient context updating in working memory (Donchin, 1981 ; Donchin & Coles, 1988 ), and/or ( b ) competition between primary-task representations and task - unrelated thoughts.

Building on this alternative perspective, we suggest that the decoupling process is a component of the switching process. As such, mind wandering could be considered as a subset of task switching that typically would run serially with task performance (i.e., serial multitasking; e.g., Huijser et al., 2018 ; for more details, see Taatgen et al., 2021 ), although a recent study has shown that mind wandering can also run parallelly with particular kinds of tasks (i.e., parallel multitasking; e.g., Brosowsky et al., 2021 ). Furthermore, in the context of serial processing of multiple mental sets, switch costs should be observed regardless of whether the shift is from external to internal (e.g., shifting from a SART to task-irrelevant personal concerns), internal to external (e.g., shifting from task-irrelevant personal concerns back to the SART), or internal to internal (e.g., shifting from mental arithmetic to task-irrelevant personal concerns). According to our view, the detrimental effect of mind wandering on primary task performance reflects the costs of switching between mental sets (i.e., decoupling from the primary task’s mental set and coupling to task - unrelated thoughts; see Fig.  1 ), in addition to the costs of not paying attention to the primary task while decoupled. This proposal is in line with research on task switching that demonstrated that switch costs could still be observed when the tasks were relatively simple, when the task sequence was predictable, and when there was a cue signaling the upcoming switch (Koch, 2003 ).

A possible way to test the process-occurrence framework

Within the process-occurrence framework, Smallwood aptly noted the following:

…the processes that ensure the continuity of the experience of an internal train of thought are similar to those that can be engaged in standard task-relevant paradigms, and as a result, these processes are becoming reasonably well understood. By contrast, our understanding of why mind wandering occurs is less well specified, in part because we are unable to identify the moment of ignition for the state. (Smallwood, 2013 , p. 532).

Indeed, as mentioned earlier, one major challenge in investigating the length of mind-wandering episodes is how to determine the “when” of mind wandering (Franklin et al., 2013 ). Although Voss et al., ( 2018 ) have provided evidence in favor of the process-occurrence framework by identifying different states and processes of mind wandering (see the “ Process-occurrence framework ” for more details), one key limitation of this study, as pointed out by the researchers themselves, was that their assessment methods hinged on the assumption that the only way for an individual to redirect their attention from mind wandering back to the task at hand is through a mechanism reliant upon self-awareness (i.e., the meta-awareness system; Schooler et al., 2011 ). If one can return to a task-focused state without relying on such a mechanism (e.g., decoupling from task-unrelated thoughts and coupling to the primary task’s mental set without conscious awareness), and can have multiple switches between task-focused and mind-wandering states during a single self-caught episode, then the estimated duration of focus and mind-wandering episodes (defined earlier in the “ Process-occurrence framework ”) could in fact reflect multiple focus-mind-wandering episodes, rather than the duration of each individual task-focused/mind-wandering state (Voss et al., 2018 ).

To shed light on how to measure the component processes of mind wandering while acknowledging that mind wandering often consumes executive resources that are needed to perform the task (i.e., parallel processing of both high cognitive demand tasks and mind wandering is difficult to achieve; Smallwood & Schooler, 2006 ; Thomson et al., 2015 ), here we put forward a new approach that links the onset of mind wandering to the onset of a new mental set, and the continuation of the mind-wandering episode to the continuation of the new mental set. Previous task-switching studies have identified a number of distinct cognitive processes underlying an attentional set switch (e.g., Meiran et al., 2000 ; Rushworth et al., 2002 , 2005 ). For instance, Rushworth et al., ( 2002 ) found that mental set shifting consisted of at least three component cognitive processes, including: ( a ) initiation of a new mental set prior to selective focusing of attention, which was indexed by an early period of ERP modulation associated with dipole source estimates in the prefrontal cortex; ( b ) reconfiguration of the new mental set, which was indexed by a later period of ERP modulation associated with dipole source estimates at the ventromedial occipitotemporal junction; and ( c ) maintenance of the new mental set and possible interference from the previous mental set, which was indexed by the N200—a negative potential over the central posterior scalp that peaks around 200 ms after stimulus presentation and is believed to be associated with response suppression (Eimer, 1993 ; Kok, 1986 ; Patel & Azzam, 2005 ). Building on these findings, we posit that at the start of the mind-wandering episode, there should be activation in the prefrontal cortex (for a review, see Zamani et al., 2022 )—a region that has been found to play a central role in cognitive flexibility (Dove et al., 2000 ; Miller & Buschman, 2013 ; Miller & Cohen, 2001 ; Sakai & Passingham, 2003 ; Sohn et al., 2000 ) and mind wandering (Bertossi & Ciaramelli, 2016 ; Burgess et al., 2007 ; Christoff et al., 2009 ; Fox et al., 2015 ; Stawarczyk & D’Argembeau, 2015 ).

Using these findings as a foundation, it seems possible that the “when” of mind wandering (i.e., the onset of mental set shifting) can be estimated, at least approximately, from activity in the prefrontal cortex measured prior to periods of self-reported mind wandering. This conjecture seems to fit with findings that non-invasive transcranial direct current stimulation of the prefrontal cortex can increase the propensity to mind wander (e.g., Axelrod et al., 2015 , 2018 ; Filmer et al., 2019 ). To clarify, we postulate that positive-polarity stimulation “encourages” the recipient to initiate a switch of mental set, which according to the resource-control account of sustained attention (Thomson et al., 2015 ) is most likely to involve a switch to a task-unrelated mental set as mind wandering is thought to be the default mental state for most individuals.

Limitations and a possible extension for the resource-control account of sustained attention

Although the resource-control account of sustained attention (Thomson et al., 2015 ), which suggests that the occurrence of mind wandering is associated with decreases in motivation and/or effort to keep attention on the task at hand over time, could explain why older adults tend to report fewer instances of mind wandering than young adults during cognitive task performance—either because they are more motivated to perform the primary task (Frank et al., 2015 ; Jackson & Balota, 2012 ; Seli et al., 2021 ; Seli, Maillet, et al., 2017 ; Seli, Ralph, et al., 2017 ) or because they have spent a larger proportion of their executive resources on the primary task (Craik & Byrd, 1982 ) and thus have fewer resources left over to exhibit mind wandering (Giambra, 1989 ; Krawietz et al., 2012 ; Maillet & Rajah, 2013 )—this theory is not without its limitations. In particular, if executive control, which wanes over time on task, is required to prevent task-unrelated thoughts (i.e., the default mental state) from consuming executive resources needed for the task at hand, then given that healthy and cognitively impaired older adults generally have poorer executive control (e.g., Flannery et al., 2018 ; Guarino et al., 2018 ; McKinlay et al., 2010 ; Ramos & Machado, 2021 ), one might reasonably expect that as time-on-task increases, these older populations would report higher incidences of mind wandering and show more pronounced performance decrements. However, this prediction was not supported by Arnicane et al., ( 2021 ), who found that in comparison to the first block (i.e., the first 15 min) of a visual working memory task, in the sixth block healthy older adults reported similar levels of attentional lapses and demonstrated improved performance. These results, therefore, are inconsistent with the predictions of the resource-control account of sustained attention, as they showed that extended task duration in fact has positive effects on healthy older adults’ working memory performance.

Here, we posit that the occurrence of mind wandering should be also associated with fluctuations in activity in brain regions associated with executive control (the frontal-parietal and dorsal attention networks; Corbetta & Shulman, 2002 ; Corbetta et al., 2008 ; Posner & Dehaene, 1994 ; Vincent et al., 2008 ) and mind wandering (the default mode network; Raichle et al., 2001 ), and that these fluctuations should be inversely related (see also Esterman & Rothlein, 2019 ). According to this account, because normal aging is associated with significant decreases in the strength of functional connectivity density (i.e., the statistical relationship between brain regions; Tomasi & Volkow, 2010 ) in the dorsal attention and default mode networks (Tomasi & Volkow, 2012 ), the lower frequencies of mind wandering reported in healthy older adults could be attributable to less efficient switching and/or cooperation between these two networks to produce a train of thought during mind wandering (Smallwood et al., 2012 ). This proposal goes beyond the resource-control account of sustained attention, which cannot account for the findings that longer task duration does not lead to a higher incidence of mind wandering or more pronounced performance decrements in healthy older adults (Arnicane et al., 2021 ). In support of this proposal, studies have shown that mind wandering is associated with increased default mode network and decreased dorsal attention network activation (Christoff et al., 2009 ; Fortenbaugh et al., 2018 ; Kucyi et al., 2013 ; Mason et al., 2007 ; Robertson et al., 1997 ; Smallwood et al., 2013 ), indicating that there might be a push–pull relationship between these two networks that impacts the occurrence of mind wandering (cf. Esterman & Rothlein, 2019 ).

We have shown that the switching perspective is a useful addition to the four prominent theories of mind wandering. While acknowledging that other factors may be at play, this newly formulated view not only provides a plausible explanation as to why healthy and cognitively impaired older adults experience a reduction in mind wandering, but it also provides new insights for determining the initiation of mind-wandering episodes. In the next section, we present evidence to support our view.

Review of evidence supporting the switching perspective

The strongest evidence to date in support of this new perspective comes from research using the voluntary task - switching paradigm (Arrington & Logan, 2004 ), for which participants are free to switch tasks or continue with the same task at their preference. Somewhat paradoxically, research has consistently demonstrated that most of the participants decided to switch tasks despite negative consequences (i.e., switch costs; e.g., Irons & Leber, 2016 ; Kessler et al., 2009 ; Mittelstädt et al., 2019 ), although comparatively healthy older adults tended to initiate voluntary task switching less frequently than healthy young adults (Ardiale & Lemaire, 2012 ; Butler & Weywadt, 2013 ; Lockenhoff et al., 2020 ; Terry & Sliwinski, 2012 ). In light of cognitive aging, this finding may not seem surprising given that repeating the currently active task set requires fewer executive resources than switching to a different task set (Wirth et al., 2018 ) and switching between task sets or rules increases cognitive load (Arrington & Logan, 2004 ; Kool et al., 2010 ). In like manner, we argue that it should not be surprising either that healthy older adults and older adults with Alzheimer’s disease, Parkinson’s disease, and amnestic mild cognitive impairment experience less frequent mind wandering, as a reduced switching ability could contribute, at least in part, to getting “stuck” in a task - focus mode (cf. Walpola et al., 2020 ).

Additional evidence for the switching perspective of mind wandering comes from the intriguing finding that mind wandering does not always impair young adults’ performance in switching tasks, in contrast to tasks that require sustained attention. Using a probe - caught method, Kam & Handy, ( 2014 ) found no significant disruptive effects of mind wandering on task-switching performance. A further study, which investigated the association between mind wandering and task-switching performance over time, observed similar response times and accuracy rates for the trials leading up to “on-task” and “off-task” reports (Thomson et al., 2014 ), which again suggests that switching performance was unaffected by mind wandering. Similar results were obtained by Arnau et al., ( 2020 ), who investigated electrophysiological correlates of mind wandering during a switching task and did not observe slower response times during periods of self-reported mind wandering relative to on-task episodes.

The lack of performance costs, particularly response time costs, for switching tasks is puzzling because mind wandering has consistently been found to disrupt behavioral performance on tasks that tap the other two core executive function measures—inhibition (e.g., Kam & Handy, 2014 ; Smallwood et al., 2008 ; Stawarczyk et al., 2011 ) and working memory (e.g., Kam & Handy, 2014 ; Krimsky et al., 2017 ; Unsworth & Robison, 2016 ). Given this, one might expect that mind wandering should also significantly affect one’s task-switching performance. Although Kam & Handy, ( 2014 ) speculated that the null effect of mind wandering on switching-task performance might reflect cognitive flexibility being a less representative executive functioning skill (as it showed the weakest correlations with other executive function measures; for more details, see Miyake et al., 2000 ), these researchers also noted that switching from the task at hand to task-unrelated thoughts may be a form of switching. In the same manner, we posit that because switching either between task-related mental sets or between task-related and task-unrelated mental sets requires cognitive flexibility, when one mind wanders during performance of a switching task, they continue to engage in a “task-switching mind frame” (i.e., instead of switching between task-related mental sets, the individual switches between task-related and task-unrelated mental sets), and thereby can maintain task-switching performance. This conjecture appears to fit well with previous studies indicating that frequent task/response switches can shift the flexibility-persistence balance (Hommel, 2015 ) towards higher flexibility (e.g., Fröber & Dreisbach, 2017 ; Fröber et al., 2018 ; Zhuo et al., 2021 ; for a review, see Dreisbach & Fröber, 2019 ).

Future directions

Acknowledging that instances of mind wandering are instances of mental set shifting (see Murray & Krasich, 2020 , for a similar argument) opens up new avenues for future scientific investigations. First, to directly investigate this new perspective, future research could examine the association between cognitive flexibility and the tendency to mind wander, as despite a number of researchers using a task - switching paradigm as the primary task in their investigation of mind wandering (e.g., Arnau et al., 2020 ; Kam & Handy, 2014 ; Thomson et al., 2014 ), to our knowledge none have explicitly investigated the role of cognitive flexibility in mind wandering. Second, as there are many different types of switching (e.g., rule switching, task set switching, and response set switching), which have been found to activate different brain areas (Ravizza & Carter, 2008 ), it may be important to investigate how these switching abilities are related and which type is most closely associated with mind wandering. Determining this may help advance the current understanding of the higher incidence of mind wandering in ADHD, and may ultimately shed light on the inconsistent results regarding whether ADHD is associated with deficits in cognitive flexibility (e.g., Halleland et al., 2012 ; Irwin et al., 2019 ; Rohlf et al., 2012 ; Willcutt et al., 2005 ), which in turn may shed further light on the viability of the switching perspective forwarded here. Third, as an increasing number of studies have revealed distinct effects of intentional and unintentional mind wandering on task performance (e.g., Martínez-Pérez et al., 2021 ; Moran et al., 2021 ; Seli et al., 2016a , 2016b ; for a review, see Seli, et al., 2016a , 2016b ), future research could investigate whether intentional and unintentional mind wandering constitute distinct forms of task-set activation (e.g., counscious or uncounscious activation of task-unrelated mental sets; Arango-Muñoz & Bermúdez, 2021 ; Lau & Passingham, 2007 ; Reuss et al., 2011 ; Weibel et al., 2013 ) that involve distinct neural mechanisms and might differ with respect to their relationships with cognitive flexibility.

Fourth, considering that in a real-world setting we constantly multitask (e.g., writing an email while listening to music and eating a meal), and studies have found a positive association between self-reported frequency of concurrent use of multiple digital media streams and mind-wandering tendency (e.g., Kane et al., 2017 ; Ralph et al., 2014 ; Wiradhany & Koerts, 2021 ), future research could investigate the association between mind wandering, multitasking, and cognitive flexibility. Fifth, to understand past findings (e.g., McVay & Kane, 2009 ; Robison & Unsworth, 2018 ; Unsworth & Robison, 2020 ) in light of this switching perspective, future studies could investigate whether healthy young adults with higher self-reported mind-wandering tendencies have lower working memory capacity but superior switching abilities. In consideration of previous findings (e.g., McVay & Kane, 2009 , 2012b ; Miyake et al., 2000 ; Oberauer et al., 2003 ; Robison & Unsworth, 2018 ; Shipstead et al., 2015 ; Unsworth & Robison, 2020 ; as discussed in the “ Limitations and an alternative viewpoint for the executive failure hypothesis ”), the increased mind - wandering frequency seen in healthy young adults with lower working memory capacity could reflect a tendency to initiate more switches between task-related and task-unrelated mental sets in relation to superior switching abilities. Sixth, considering that the executive failure hypothesis (McVay & Kane, 2009 , 2010 , 2012b ) fits with the data in healthy young adults and individuals with ADHD, but does not account for the data in older adult populations discussed in this review, future research should explore the association between cognitive flexibility and the tendency to mind wander in different populations with age in mind, as it could be the case that the executive failure hypothesis applies to young adults whereas the switching account of mind wandering applies to older populations.

Concluding remarks

The findings reviewed in this article provide initial evidence to suggest that there may be an association between cognitive flexibility and mind wandering, and that distinct patterns of mind wandering may signal and be a product of altered cognitive flexibility. Although more research is needed, the switching perspective of mind wandering put forward here may provide a more comprehensive account of mind wandering that fits better with the experimental findings to date, including why healthy older adults and those with Alzheimer’s disease, Parkinson’s disease, and amnestic mild cognitive impairment experience less mind wandering (e.g., Gyurkovics et al., 2018 ; Jordão et al., 2019 ; Maillet & Schacter, 2016 ; Niedzwienska & Kvavilashvili, 2018 ; Walpola et al., 2020 ). This novel line of research may lead to the development of clinical detection tools and therapeutic approaches (e.g., task - switching training) aimed at preserving, or enhancing, the rates of mind wandering in populations with reduced levels of mind wandering (e.g., older adults), as mind wandering does have important functions such as facilitating future planning (e.g., Baird et al., 2011 ; Mazzoni, 2019 ; Seli, Maillet, et al., 2017 ; Seli, Ralph, et al., 2017 ; Stawarczyk et al., 2011 ) and the generation of creative ideas (e.g., Gable et al., 2019 ; Yamaoka & Yukawa, 2020 ) as well as promoting positive mood (e.g., Welz et al., 2018 ). Furthermore, recognizing that previous findings on mind wandering can be viewed from a switching perspective may provide an important contribution to our understanding of the basic psychological processes of mind wandering and its determinants, and may help future research to come up with a definition of mind wandering that will gain consensus in the field.

Data availability

Not applicable.

Code availability

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Y. - S.W. acknowledges the receipt of a PhD scholarship from the University of Otago. We thank Dr Marijn Kouwenhoven and Wayne Meighan for comments on earlier drafts. We have no known conflict of interest to disclose.

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Wong, YS., Willoughby, A. & Machado, L. Reconceptualizing mind wandering from a switching perspective. Psychological Research 87 , 357–372 (2023). https://doi.org/10.1007/s00426-022-01676-w

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Mind-Wandering With and Without Intention

1 Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA

Evan F. Risko

2 Department of Psychology, University of Waterloo, Waterloo, ON, Canada

Daniel Smilek

Daniel l. schacter.

The past decade has seen a surge of research examining mind-wandering, but most of this research has not considered the potential importance of distinguishing between intentional and unintentional mind-wandering. However, a recent series of papers has demonstrated that mind-wandering reported in empirical investigations frequently occurs with and without intention, and more critically, that intentional and unintentional mind-wandering are dissociable. This emerging literature suggests that to increase clarity in the literature, there is a need to reconsider the bulk of the mind-wandering literature with an eye toward deconvolving these two different cognitive experiences. In this review, we highlight recent trends in investigations of the intentionality of mind-wandering and outline a novel theoretical framework regarding the mechanisms underlying intentional and unintentional mind-wandering.

The Intentionality of Mind-Wandering

Research on mind-wandering has seen a massive increase in recent years, spreading to a wide variety of psychological domains including those examining cognition [ 1 – 10 ], neuroscience [ 11 – 16 ], education [ 17 – 20 ], creativity [ 21 , 22 ], clinical populations [ 23 – 26 ], and workplace functioning [ 27 ], to name a few. The rapidly growing body of research on mind-wandering was largely stimulated by Smallwood and Schooler’s [ 28 ] integrative review of related concepts such as ‘task-unrelated imagery and thoughts (TUITs)’ [ 29 ] and ‘stimulus-independent thought’ [ 30 ]. Although the unification of these related concepts under the single term ‘mind-wandering’ has proven to be exceptionally useful in stimulating research, the field has now advanced to a point where it is necessary to make finer distinctions and to consider different types of mind-wandering [ 31 ]. One such distinction, originally advanced prior to the recent surge of research focused on “mind-wandering,” is that between intentional task-unrelated thought and unintentional task-unrelated thought [ 32 ] (see Box 1 ). Although this distinction has been largely ignored since its inception, an emerging area of research focused on the intentionality of mind-wandering has clearly demonstrated the practical and theoretical utility of making this distinction. In this review, we discuss this recent trend, and make the case that the distinction between intentional and unintentional mind-wandering is becoming, and ought to continue to be, a prominent focus in research on mind-wandering.

Differences between Unintentional and Intentional Mind-Wandering

An early distinction between unintentional and intentional task-unrelated imagery and thoughts (TUITs) was advanced over 20 years ago by Giambra and colleagues [ 29 , 32 , 39 ]. Giambra [ 29 ] noted “TUITs may occupy awareness because they capture our attention – an uncontrolled shift – or because we have deliberately shifted our attention to them – a controlled shift” (p. 2). He also identified several ways in which these two types of attentional shifts differ. According to Giambra [ 29 ]:

“Voluntary shifts of attention to TUITs would seem to involve higher orders of control in information processing or be motivationally determined and to be benign because of their controlled nature. However, involuntary shifts of attention from the task at hand to TUITs would seem to involve lower orders of control in information processing and not [be] motivationally determined; in addition, involuntary shifts may be less benign because they are uncontrolled” (p. 2).

To these differences between intentional and unintentional TUITs (or mind-wandering), we could also add possible differences in subjective experiences. Unintentional episodes of mind-wandering lack a distinct moment of conscious initiation, and during these episodes, participants are likely not meta-cognitively aware that they are mind-wandering. Consequently, once the episode is detected, the participant might experience surprise, vexation, and the feeling of a lack of control. In contrast, intentional episodes of mind-wandering are associated with a conscious moment of intention to initiate (or to continue) a mind-wandering episode. Moreover, intentional mind-wandering likely includes metacognitive awareness of its occurrence (at least at some point during the episode) and is therefore unlikely to be associated with surprise at or vexation, or the feeling of a lack of control.

Interestingly, although unintended, episodes of unintentional mind-wandering are nevertheless experienced as being authored by the individual (in the sense of authorship noted by [ 80 ]) and are thus accompanied by a sense of agency. In other words, unintentional mind-wandering is not experienced as being derived from an alien source, but as originating within the individual. In this way, unintended mind-wandering is similar to non-deliberate action. With respect to non-deliberate action, Bayne and Levy [ 81 ] note: “Few of our actions derive from processes of conscious deliberation, and there is no reason to think that those actions that are non-deliberative are any less authored than those that are.” Nevertheless, an interesting direction for future research is to examine how people’s feeling of authorship (i.e., agency) differs between intentional and unintentional bouts of mind-wandering.

Mind-Wandering Can Occur With or Without Intention

Although mind-wandering was initially defined as off-task thought that occurs either with or without intention [ 28 ], some researchers have assumed that the mind-wandering they have examined in their investigations occurred without intention [ 2 , 11 , 33 – 38 ]. At face value, this seems to be a reasonable assumption. That is, when participants enter the laboratory, there is a tacit assumption that they will do their best to attend to the assigned tasks. Hence, in cases where participants report the experience of mind-wandering during task completion, it would be reasonable for a researcher to assume that this mind-wandering occurred despite the participants’ best intentions to remain focused on the task (i.e., that it occurred unintentionally). Notwithstanding the apparent soundness of this assumption, it has been challenged by recent studies that have validated and extended previous work [ 32 , 39 ] showing that, in the laboratory, people’s mind-wandering episodes are frequently engaged with intention [ 40 – 44 ].

The finding that people frequently report intentional mind-wandering was revealed in a series of experiments that examined rates of mind-wandering while participants completed behavioral tasks. To capture moments during which people intentionally and unintentionally mind-wandered, these experiments have relied upon a variant of the commonly used experience-sampling technique, which involves periodically presenting participants with “thought probes” while they complete an ongoing task. Although thought probes traditionally require participants to periodically report whether they are focused on the current task or mind-wandering, to examine the intentionality of mind-wandering episodes, recent studies have required participants to instead report whether they were (1) focused on the task, (2) intentionally mind-wandering, or (3) unintentionally mind-wandering [ 40 , 44 ]. Despite the fact that some researchers have come to assume that laboratory-based mind-wandering reflects unintentionally engaged off-task thought, 34% to 41% of the mind-wandering that participants reported while completing these laboratory tasks has been engaged with intention ([ 41 , 45 ], respectively). Although other studies have found comparatively less intentional mind-wandering in laboratory tasks [ 40 ], the fact that, in at least some tasks, a substantial portion of mind-wandering occurs intentionally suggests that participants may not be particularly motivated to complete some psychological tasks, or that they may perceive the tasks to be sufficiently easy that they believe they can afford to mind-wander without hindering performance (see Box 2 ).

Implications of the Finding that People Frequently Intentionally Mind-Wander

That participants frequently intentionally mind-wander is important because it indicates that they may, in some (but not all) situations, have low motivation to perform the kinds of tasks that are frequently used by mind-wandering researchers. This is, however, somewhat ironic because many researchers have largely been interested in examining unintentional mind-wandering [ 33 – 35 , 82 – 89 ], and to this end, they have often presented participants with tedious and boring tasks to elicit unintentional mind-wandering (e.g., sustained-attention and vigilance tasks; [ 14 , 67 , 90 – 95 ]). However, given that (1) people’s level of motivation tends to be rather low when they experience boredom [ 96 , 97 ], and (2) decreases in motivation levels are associated with increases in intentional mind-wandering [ 41 ], it might be that the common employment of boring tasks in the context of research on mind-wandering has inadvertently elicited intentional mind-wandering. The implication, then, is that researchers who are specifically interested in examining unintentional mind-wandering might want to more carefully consider the type of tasks they choose to administer in their investigations.

Although rates of intentional mind-wandering appear to be particularly high in cases where researchers employ relatively boring tasks – as is typically done in research investigating mind-wandering – it appears that intentional mind-wandering also rears its head during other, ostensibly less boring, tasks that might be of interest in other domains of psychological research. For instance, one study [ 45 ] showed that participants viewing a video-recorded lecture in the laboratory reported intentional mind-wandering to roughly 10% of thought probes (with similar rates of intentional mind-wandering reported during reading tasks; [ 51 ]). Because thought probes provide an estimate of the total amount of time participants spend mind-wandering during laboratory tasks, this finding indicates that participants spent about 10% of their time deliberately disengaging from the task in the service of mind-wandering, or about three of the 30 minutes that it took to complete the task. Thus, beyond having implications for the field of mind-wandering, the finding that people intentionally mind-wander in the laboratory while completing various psychological tasks might be important for the field of psychology as a whole. Indeed, although it is commonly assumed that participants are motivated to be attentive to the psychological tasks they are given, it appears that they spend a considerable amount of time deliberately thinking about things other than the task.

Complementing these state-level findings, research that has investigated rates of mind-wandering at the trait level has revealed a similar pattern of results. Because investigations of mind-wandering at the trait level are concerned with peoples’ reports of their everyday experiences of mind-wandering, these studies have often assessed the intentionality of mind-wandering by administering questionnaires that require people to retrospectively report the extent to which they have engaged in intentional and unintentional mind-wandering in their daily lives [ 23 , 42 , 46 , 47 ], although some researchers have more directly assessed rates of intentional and unintentional mind-wandering in daily life, while people were engaged in everyday activities ([ 48 – 50 ]; see Box 3 ). Critically, research administering such questionnaires has reported that people frequently engage in intentional mind-wandering in everyday life, and that these rates actually exceed those of unintentional mind-wandering [ 42 , 46 , 47 ]. Moreover, recent research has shown positive correlations between state- and trait-level reports of intentional and unintentional mind-wandering [ 42 ]: That is, individuals who more frequently report intentional mind-wandering in their everyday lives also more frequently report intentional mind-wandering when probed during behavioural tasks in the laboratory. Likewise, individuals who more frequently report unintentional mind-wandering in their everyday lives tend to report more unintentional mind-wandering in the laboratory. Importantly, this work has provided evidence for the construct validity and the generalizability of both trait and state indices of mind-wandering.

Intentional and Unintentional Mind-wandering in Everyday Life

One study [ 48 ] examined people’s rates of mind-wandering in everyday life by administering a ‘daily-life experience-sampling protocol’ that required participants to respond, via a Palm Pilot PDA, to questionnaires pertaining to their cognitive activity as they went about their daily routines. In particular, over the course of a week, between noon and midnight, participants’ Palm Pilots would beep to signal them to complete eight questionnaires. Critically, included among the questionnaires was an item pertaining to participants’ rates of intentional mind-wandering: “I allowed my thoughts to wander on purpose.” This item, presented in any cases where participants indicated that they were mind-wandering, was endorsed with a mean response of 4.06 on scale from 1 to 7 (1 = not at all, 7 = very much), suggesting that in everyday life, intentional mind-wandering is a relatively common event.

In more recent work investigating mind-wandering in everyday life, researchers [ 49 ] examined the intentionality of mind-wandering of students enrolled in a large undergraduate course. Students enrolled in the course were queried about their mind-wandering during almost every class across an entire semester. Importantly, at various points throughout the lectures, students were asked to report whether they were “on task,” “intentionally mind-wandering,” or “unintentionally mind-wandering.” Results indicated that participants reported mind-wandering roughly 34% of the time, with slightly more than half of their mind-wandering episodes occurring intentionally (see Figure I ). Thus, again, this finding suggests that in everyday life, people frequently experience intentional mind-wandering.

Mean proportion of the thought probes to which participants responded that they were engaging in overall (either intentional or unintentional), intentional, or unintentional mind-wandering (squares), as well as individual data points for each student included in the analysis (circles). Error bars reflect 95% confidence intervals. Adapted from [ 49 ].

Dissociating Intentional and Unintentional Mind-Wandering

Although the foregoing research has clearly demonstrated that people experience and report intentional and unintentional mind-wandering at both the trait and state levels, it is important to evaluate whether these two types of mind-wandering behave differently. Showing that unintentional and intentional mind-wandering are sometimes dissociable would indicate the importance of distinguishing between the two and present an important challenge for previous research working under the assumption of a unitary (unintentional) view of mind-wandering. Importantly, recent research has shown that these two types of mind-wandering are sometimes (although not always [ 47 ]) dissociable in that they are (1) uniquely associated with certain individual-differences variables [ 25 ], and (2) differentially influenced by certain experimental manipulations [ 44 , 51 ].

Individual Differences in Intentional and Unintentional Mind-wandering

Research examining possible dissociations of intentional and unintentional mind-wandering at the individual-differences level has shown that these two types of mind-wandering sometimes independently predict variables of theoretical interest. For example, intentional and unintentional mind-wandering have been found to be differentially associated with attention-deficit/hyperactivity disorder (ADHD; [ 23 ]) and obsessive-compulsive disorder (OCD; [ 25 ]). In particular, whereas individuals reporting higher rates of unintentional mind-wandering also report more symptoms associated with ADHD and OCD, research has failed to observe a relation between rates of intentional mind-wandering and ADHD/OCD symptomatology. In addition, it has been shown that people’s level of motivation to perform well on a given task is negatively associated with rates of intentional mind-wandering during that task, whereas the link between motivation and unintentional mind-wandering appears to be less robust [ 41 , 45 ].

Collectively, this research has demonstrated that there are cases in which individual-differences variables are more strongly associated with unintentional than intentional mind-wandering (e.g., ADHD and OCD), and conversely, cases in which such variables show the exact opposite pattern of results (e.g., motivation). Moreover, there has been at least one demonstration of a situation in which an individual-differences variable shared opposing unique associations with intentional and unintentional mind-wandering. Specifically, whereas rates of intentional mind-wandering uniquely positively predict people’s tendency to be non-reactive to their inner experiences (an aspect of mindfulness), rates of unintentional mind-wandering uniquely negatively predict this same factor [ 47 ]. Taken together, these dissociations indicate that intentional and unintentional mind-wandering are sometimes uniquely associated with certain individual-differences variables, which suggests the importance of separately assessing trait levels of these two types of mind-wandering.

State-Level Dissociations of Intentional and Unintentional Mind-wandering

Recent research has also shown that intentional and unintentional mind-wandering can behave quite differently under certain experimental manipulations. For example, one study [ 51 ] combined a re-reading manipulation with a thought-probe measure of mind-wandering and found that individuals mind-wandered more while re-reading compared to an initial reading. If the researchers had stopped there, they would have concluded, in line with extant literature, that rereading makes it more difficult to prevent our minds from unintentionally wandering away from the task. Although this is not an unreasonable conclusion to draw, it turns out to be incorrect: Follow-up experiments using probes that indexed the intentionality of mind-wandering revealed that the effect of re-reading on mind-wandering was driven completely by an increase in intentional mind-wandering, and that re-reading had no influence on unintentional mind- wandering.

In a similar vein, it was recently demonstrated that manipulations of task difficulty can have opposing effects on intentional and unintentional mind-wandering: whereas participants reported more intentional mind-wandering in an easy task than in a difficult task, they reported more unintentional mind-wandering in a difficult task than in an easy task [ 44 ]. This latter finding is particularly important because it reinforces the idea that the standard practice of conflating intentional and unintentional mind-wandering will likely produce underspecified or even incorrect conclusions, as it would have in the re-reading study discussed above. Indeed, although rates of intentional and unintentional mind-wandering varied across the easy and the difficult condition, there was no difference in rates of mind-wandering across conditions when the intentionality of the episodes was ignored (as is the standard practice in the field). Hence, had the researchers not distinguished between intentional and unintentional mind-wandering, they would have drawn the incorrect conclusion that the task-difficulty manipulation did not affect rates of mind-wandering. Importantly, given that the vast majority of research on the topic of mind-wandering has not distinguished between intentional and unintentional types, this and other related findings suggest the possibility that some of the conclusions drawn in previous studies were incorrect or at least underspecified.

Taken together, the foregoing findings provide evidence that (1) challenges the notion that mind-wandering is a unitary construct that exclusively reflects unintentional thought, and (2) suggests that intentional and unintentional mind-wandering reflect unique, dissociable constructs that can behave differently in empirical investigations (see Figure 1, Key Figure ).

(A) Mean proportion of the thought probes to which participants reported overall mind-wandering (the sum of intentional and unintentional mind-wandering), intentional mind-wandering, or unintentional mind-wandering. Error bars are ± 1 SEM . Adapted from [ 41 ]. (B) Mean proportion of mind-wandering type (intentional, unintentional) reported during an easy and a difficult sustained-attention task. Participants reported more intentional mind-wandering in the easy than in the difficult condition, and conversely, more unintentional mind-wandering in the difficult than in the easy condition. Error bars are ± 1 SEM . Adapted from [ 44 ] (reprinted with permission from Psychological Science). (C) Scatterplot showing a non-significant relation between mean trait-level reports of intentional mind-wandering (residualized on trait-level reports of unintentional mind-wandering) and mean attention-deficit/hyperactivity disorder (ADHD) symptomatology, assessed by the Adult Self-Report ADHD scale (ASRS). Adapted from [ 23 ] (reprinted with permission from Springer Nature Publishing Group). (D) Scatterplot showing a significant positive relation between mean trait-level reports of unintentional mind-wandering (residualized on trait-level reports of intentional mind-wandering) and mean ADHD symptomatology, assessed by the ASRS. Adapted from [ 23 ] (reprinted with permission from Springer Nature Publishing Group).

Intentionality in Existing Models of Mind-Wandering

As one might surmise from the discussion above, to date the topic of intentionality has not been addressed in theoretical models of mind-wandering. Although this absence of intentionality does not necessarily commit these models to the idea that intentional mind-wandering does not exist, it does carry with it the implicit assumption that separate mechanisms are not required to explain the maintenance and occurrence of these two types of mind-wandering. For example, one prominent model of mind-wandering, known as the Executive-Control-Failure account [ 2 ], posits that mind-wandering results from a failure of working memory to control or suppress interfering thoughts. Here, the focus on “failures of control” suggests that this model was specifically intended to provide an explanation of unintentional mind-wandering (although there remains the possibility that this model was intended to accommodate both intentional and unintentional mind-wandering while attributing both types of mind-wandering to the same mechanism; i.e., a failure of control). Either way, specific consideration of intentional and unintentional mind-wandering has, to date, been absent from this model.

Another prominent model, known as the Attentional-Resources account [ 28 ], posits that instead of reflecting a failure of executive control, mind-wandering actually requires the engagement of executive-control resources for its sustenance. This account remains agnostic with respect to the role of intentionality in mind-wandering; hence, it does not specify any unique mechanism(s) associated with intentional and unintentional mind-wandering, nor does it make predictions regarding the intentionality of mind-wandering.

Lastly, a more recently advanced theoretical framework for mind-wandering, referred to as the Process-Occurrence Framework [ 52 ], distinguishes between the initiation and the continuation of a mind-wandering episode, linking the Executive-Control-Failure account to the moment of initiation, and the Attentional-Resources account to the continuation of the episode. Although this framework has provided much clarity in the literature by resolving the apparent conflict between the Attentional Resource and Executive Control Failures accounts, it does not appear to add any specification regarding the role of intentionality of mind-wandering that goes beyond the two former accounts.

As presently construed, then, the dominant models of mind-wandering can be argued to suggest that, although mind-wandering may occur both intentionally and unintentionally, these two types of mind-wandering do not require separate theoretical treatment. Of course, this interpretation is largely based on the tendency of existing models to refrain from explicitly addressing the distinction between unintentional and intentional mind-wandering, not the explicit denial of the existence of intentional and unintentional mind-wandering. Nonetheless, an important direction for the literature on mind-wandering will be to determine whether a single set of mechanisms can be used to explain these two types of mind-wandering.

A Framework for Understanding Intentional and Unintentional Mind-Wandering

In considering the potential mechanisms underlying intentional and unintentional mind-wandering, it may be useful to build on an existing distinction between volitional and reflexive shifts of attention, which has played a major role in general theories of human attention [ 53 ]. For instance, Kahneman’s [ 54 ] classic Capacity Model of Attention includes an “allocation policy” that controls the distribution of “available capacity” to various tasks, and this allocation policy is believed to be governed by several factors including “enduring dispositions which reflect the rules of involuntary attention” and “momentary intentions” (p. 11). Similarly, theories of spatial attention distinguish between endogenous and exogenous shifts of attention among different types of content [ 55 ]: Endogenous shifts of attention are said to be volitional and guided by “top-down” goals, whereas exogenous shifts are believed to reflect involuntarily shifts of attention initiated by salient external stimuli in a “bottom-up” manner (e.g., an abrupt onset; [ 56 ]).

The distinction between volitional and reflexive attention in existing theories of human attention can be thought of as corresponding to intentional and unintentional mind-wandering in the following way: Intentional control over mind-wandering and endogenous control over attention would both be characterized as involving a ‘willful’ or ‘volitional’ shift to some content, which is internal in the former case and external in the latter case [ 57 ]. At the same time, unintentional mind-wandering and exogenous control of attention would both reflect the process of attentional capture, despite people’s best attempts to focus their attention on their current task. In the case of exogenous capture, the “capturing” stimulus is thought to be an external stimulus (e.g., a loud noise). However, in the case of unintentional mind-wandering, this ‘stimulus’ could be a node of high activation in one’s semantic network that is, at the time, below the threshold of awareness [ 2 ]. These nodes of high activation (or perhaps low threshold) might correspond to current concerns [ 58 ] or recently primed concepts [ 59 ].

From this perspective, the reported dissociations between intentional and unintentional mind-wandering could be re-interpreted as reflecting the engagement of different attentional-control networks. For example, the selective effect of re-reading on intentional mind-wandering could be construed as an endogenous shift of attention away from the reading that is prompted by the realization that comprehension of the text during the second reading should require fewer attentional resources and less effort [ 60 ]. Furthermore, the relation between OCD/ADHD and unintentional mind-wandering could reflect the more frequent occurrence of salient, and thus exogenously attention-capturing, self-generated thoughts in these special populations.

In addition to providing a framework for explaining existing results, drawing parallels between intentional and unintentional mind-wandering, and endogenous and exogenous shifts of attention (respectively) could lead to novel predictions. For example, these two modes of attentional control have been associated with two distinct neural systems (for reviews, see [ 61 , 62 ]), with endogenous control being associated with a dorsal frontoparietal network (including the frontal eye fields and the intraparietal sulcus/superior parietal lobule) and exogenous control being associated with a ventral frontoparietal network (including the temporoparietal junction and the ventral frontal cortex). Given these findings, it seems possible that intentional and unintentional mind-wandering also engage distinct neural systems, with the possibility that there might even be some overlap between these networks and the exogenous/endogenous networks involved in spatial shifts of attention (see Box 4 ).

Mind-Wandering and the Brain

A growing body of work has identified various brain regions that are most active during episodes of mind-wandering relative to periods of on-task thought. A recent meta-analysis of neuroimaging studies examining mind-wandering [ 35 ] confirms that the brain regions involved in overall mind-wandering overlap with areas associated with default-mode-network (DMN; a network that is active when participants do not have a task to complete), as well as the frontoparietal-control network (a network associated with executive control). The areas involved in mind-wandering, and those typically involved in the DMN and frontoparietal-control network, are shown in Figure I (taken from [ 35 ]). The idea that frontal executive-control regions are involved in mind-wandering is also supported by recent studies showing that transcranial-direct-current stimulation (tDCS) applied to the lateral prefrontal cortex can modulate the amount of reported mind-wandering [ 33 , 98 ].

The involvement of the control network in mind-wandering is particularly noteworthy. It has been argued that the control network is coopted by the mind-wandering episode, such that the “executive control regions guide, evaluate, and select among the various spontaneous streams of thoughts, memories, and imaginings offered up to consciousness by the DMN” [ 35 ]. If mind-wandering is exclusively unintentional, as sometimes assumed, then the idea that control regions support mind-wandering is important because executive processes are often associated with intentional direction of thought. Indeed, co-operation between the frontoparietal control network and the DMN, similar to that observed during mind-wandering, has been reported in goal-directed cognitive tasks such as autobiographical planning [ 99 ].

However, it is important to note that the neuroimaging and tDCS studies that implicate control regions in mind-wandering did not distinguish between intentional and unintentional mind-wandering. In light of the prevalence of intentional mind-wandering in laboratory tasks, and given the boring conditions typically experienced in neuroimaging studies, it seems reasonable to assume that at least some of the observed mind-wandering has been largely intentional. This observation raises the interesting possibility that activation of the executive-control regions might be mostly reflective of intentional, rather than unintentional, mind-wandering. Indeed, consideration of the fact that mind-wandering can involve intentional or unintentional modes of control, together with the observation that there are two modes of control over spatial attention [ 55 ], suggests the possibility that the existing neuroimaging findings have conflated two distinct control networks (intentional and unintentional). Thus, an important direction for future research will be to assess the potentially distinct neural correlates of intentional and unintentional mind-wandering.

Significant meta-analytic clusters of brain activity associated with periods of mind-wandering (green clusters) contrasted with the DMN (blue) and frontoparietal control network (red). Meta-analytic activity associated with mind-wandering shows marked overlap with both the DMN and frontoparietal control network. DMN and frontoparietal control network masks are based on aggregate data from 1000 subjects, as reported by [ 100 ]. Taken from [ 35 ].

Moreover, construing intentional and unintentional mind-wandering as reflecting the action of different control networks raises the interesting issue of what factors might influence these different modes of control. For instance, intentional mind-wandering might be heavily influenced by factors such as (1) an individual’s level of intrinsic motivation to complete a task (i.e., whether tasks are ‘have-to’ or ‘want-to’ tasks; [ 63 , 64 ]), (2) an individual’s level of interest in a task [ 65 ], (3) an individual’s metacognitive beliefs about the extent to which mind-wandering influences performance, and (4) an individual’s ability and inclination to strategically mind-wander during moments of low task demand [ 37 , 66 , 67 ]. In contrast, unintentional mind-wandering might be more heavily influenced by (1) the amount of unused resources available for task-unrelated processing [ 68 ], (2) the gravity of personal concerns [ 58 ], (3) ‘opportunity costs’ that arise while completing a task [ 69 ], and (4) an individual’s decision to take an ‘active’ (intense) or ‘passive’ (relaxed) stance towards attentional control in a given situation [ 54 ]. It is important to note, however, that we are not ruling out the possibility that some of the foregoing factors might influence both intentional and unintentional mind-wandering; indeed, there is good reason to believe that many factors will have some effect on both modes of control. For instance, low motivation to complete a particular task might lead to deliberate shifts of attention toward mind-wandering and also make it easier for activation in the semantic network to unintentionally attract attention, thus pulling attention from a primary task.

In discussing parallels between intentional and unintentional mind-wandering, and endogenous and exogenous shifts of attention, it should be noted that intentionality during mind-wandering need not be restricted to the initiation of a mind-wandering episode. Indeed, control over the distribution of resources likely dynamically unfolds over time [ 70 ]. As such, intentional mind-wandering can also manifest as an allowance of the continuation of a previously unintentionally progressing episode (e.g., endogenous maintenance of attention at a location that was arrived at exogenously). Similarly, unintentional mind-wandering can manifest as an intended episode of mind-wandering that has gone beyond an intended stopping point (e.g., exogenous maintenance of attention at a location that was arrived at endogenously).

The Practical Benefits of Assessing the Intentionality of Mind-Wandering

Beyond having important theoretical implications, the finding that people frequently engage in intentional mind-wandering might play a pivotal role in practical research aimed at developing methods to reduce the occurrence of mind-wandering. Although mind-wandering has been associated with certain beneficial outcomes (e.g., autobiographical planning, creativity [ 71 ]), it has also been associated with numerous serious negative consequences (e.g., car accidents, deficits in reading comprehension, problems with workplace functioning [ 71 ]). Thus, it is perhaps unsurprising that researchers have been eager to develop methods with which rates of mind-wandering can be reduced in daily life. To date, a few such methods have been identified. First, working under the theory that mind-wandering is sometimes the result of unfulfilled goals hijacking one’s attention, one study showed that instructing people to formulate specific plans aimed at resolving their unfulfilled goals led to a subsequent reduction in mind-wandering or “intrusive thoughts” [ 72 ]. In another study, researchers had participants engage in mindfulness meditation, showing that this practice minimized subsequent rates of mind-wandering [ 73 ].

As researchers continue to investigate methods with which to reduce the occurrence of mind-wandering, it will be important for them to consider how different methods of remediation might vary in their effectiveness depending on whether the mind-wandering in question is intentional or unintentional. In particular, the specific methods that minimize the occurrence of these two types of mind-wandering might well differ. For example, in the context of a lecture, unintentional mind-wandering might be reduced by increasing the salience of the presented material [ 74 , 75 ], perhaps by including stimulating presentation slides or interesting videos. Thus, carving mind-wandering at this joint might provide fruitful grounds for future research to identify methods of remediation.

Concluding Remarks and Future Perspectives

The findings reviewed here provide strong evidence to suggest that people’s minds wander both with and without intention. This research has revealed that unintentional and intentional mind-wandering are differentially associated with various individual-differences variables, and can be dissociated experimentally. These dissociations suggest that these two types of mind-wandering might be associated with different underlying mechanisms. Although speculative at present, these different underlying mechanisms might profitably be viewed as being analogous to the long-held distinction between exogenous and endogenous attentional control.

The emergence of the distinction between intentional and unintentional mind-wandering has also opened the door to numerous opportunities for future research. At the most basic level, the majority of research to date has used dichotomous probes to measure mind-wandering: a methodology that we now know glosses over the distinction between unintentional and intentional mind-wandering. Thus, revisiting this work with this distinction in mind promises valuable new insights. For example, individual differences in working memory are known to be related to rates of mind-wandering: Understanding whether working memory is related to unintentional, intentional, or both types of mind-wandering would provide valuable constraints on existing theory (i.e., the Executive-Control Failure Account; [ 2 ]). Beyond revisiting important past work, thinking about the intentionality of mind-wandering provides both a new perspective on old questions and brings into focus completely novel lines of inquiry. In terms of providing a new perspective on old questions, the distinction between unintentional and intentional mind-wandering could help disambiguate patterns of brain activation associated with mind-wandering ( Box 4 ). In terms of generating novel questions, placing intentional mind-wandering under the microscope raises new questions about how metacognition or meta-attention might contribute to rates of mind-wandering (See Outstanding Questions ).

Outstanding Questions Box

• How do people report on the intentionality of mind-wandering? The intentionality of mind-wandering might wax and wane throughout an episode. Thus, when people report on the intentionality of mind-wandering, it is unclear which component of the episode informs this report. For example, reports might be informed by the initiation of the episode, the episode’s most recent segment, or the average time spent mind-wandering with intention. Elucidating the processes involved in informing reports of intentionality is important for our theoretical understanding of mind-wandering, and it will therefore be an important direction for future research.
• Do the methods aimed at reducing mind-wandering differentially affect intentional and unintentional mind-wandering? Researchers have been eager to develop methods with which to reduce the occurrence of mind-wandering. To date, a few such methods have been identified (e.g., mindfulness meditation). Because rates of intentional and unintentional mind-wandering behave differently under certain experimental manipulations, it will be important for research to consider how different methods of remediation might vary in their effectiveness depending on whether the mind-wandering in question is intentional or unintentional.
• What are the neural correlates of intentional and unintentional mind-wandering? Neuroimaging studies of mind-wandering have not distinguished between intentional and unintentional types. However, in line with the literature on endogenous and exogenous modes of control over attention, intentional and unintentional mind-wandering might be subserved by different attentional networks.
• Does the content of intentional and unintentional mind-wandering differ? Episodes of mind-wandering are known to vary in content (e.g., in terms of temporal focus, valence, etc.). However, it remains unknown whether the content of mind-wandering reliably differs as a function of intentionality.

Another area where the intentional/unintentional distinction could prove useful concerns the link between mind-wandering, on the one hand, and episodic or autobiographical memory and future thinking, on the other. Recent research has revealed numerous similarities between episodic memory and future thinking, including reliance on the default-mode network [ 76 , 77 ], which, as we discuss in Box 4 , has also been linked to mind-wandering. Cognitive studies suggest that during bouts of mind-wandering, people sometimes remember past experiences and, to an even greater extent, imagine future experiences [ 78 ]. However, it is currently unknown whether the relative frequencies of episodic memories and future thoughts differ during incidents of intentional versus unintentional mind-wandering. Indeed, some researchers [ 78 ] have suggested that the focus on future thoughts during mind wandering episodes suggests a possible functional role for mind-wandering in problem solving, but it is unknown whether such functionality is equally characteristic of intentional and unintentional mind-wandering. Finally, recent research on cognitive aging has also suggested a link between mind-wandering and involuntary autobiographical memory retrieval – older adults show reductions in both compared with young adults [ 79 ] – but it is unknown whether the age-related reduction in mind wandering applies to both intentional and unintentional varieties.

The distinction between unintentional and intentional mind-wandering will likely also prove useful beyond the laboratory. For example, it will be important for future work to investigate the possibility that methods aimed at reducing rates of intentional and unintentional mind-wandering might vary in terms of their effectiveness depending on the type of mind-wandering in question. This fine-grained look at the mind-wandering promises more targeted interventions, for example, in the classroom and/or workplace.

Given that intentional and unintentional mind-wandering have been shown to sometimes behave differently, making the distinction between the two would appear to have important implications for most, if not all areas of mind-wandering research. However, we do not intend to suggest that researchers ought to always examine the intentionality of mind-wandering. That said, in cases where the distinction between intentional and unintentional mind-wandering is not made, we believe that it will be important for researchers to restrict any inferences they draw to mind-wandering in more generic terms, and to acknowledge that assessments of intentionality might provide a more nuanced understanding of their findings.

Although investigations of the intentionality of mind-wandering have opened new doors, treading through those doors will not come without its challenges. One significant challenge that strikes at the heart of mind-wandering research in general pertains to whether we can trust individuals’ self-reports of their mental states. In particular, can we trust individuals’ self-reports of whether a given episode of mind-wandering was unintentional or intentional? How might task-demands or social-desirability effects influence responses, and in particular, individuals’ willingness to report intentional mind-wandering? Addressing these issues will be critical if the distinction between unintentional and intentional mind-wandering is to play a central role in the future of mind-wandering research. Notwithstanding these challenges, recent trends in examining the intentionality of mind-wandering promise both exciting future avenues of research and, ultimately, a deeper understanding of the wandering mind.

• Researchers are beginning to recognize the importance of distinguishing between intentional and unintentional forms of mind-wandering.
• The standard practice has been to employ dichotomous probes that ask people to report whether they are “on task” or “mind-wandering,” which conflates intentional and unintentional types of mind-wandering.
• A growing number of studies have shown that people intentionally mind-wander both in laboratory tasks and in everyday life, and that intentional and unintentional mind-wandering are dissociable cognitive experiences.
• Extant theories have largely neglected the distinction between unintentional and intentional mind-wandering and must be amended to include the important role of intentionality.

Acknowledgments

P.S. was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Post-Doctoral Fellowship. E.F.R was supported by funding from the Canada Research Chairs program 04532. D.S. was supported by an NSERC discovery grant 06459. D.L.S was supported by a National Institute on Aging RO1 AG08441.

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• Published: 15 May 2018

Mind Wandering and Task-Focused Attention: ERP Correlates

• Óscar F. Gonçalves   ORCID: orcid.org/0000-0003-2735-9155 1 , 2 , 3 ,
• Gabriel Rêgo 3 ,
• Tatiana Conde 3 , 4 ,
• Jorge Leite 1 , 2 , 5 ,
• Sandra Carvalho 1 , 2 ,
• Olívia Morgan Lapenta 3 , 6 &
• Paulo S. Boggio 3  

Scientific Reports volume  8 , Article number:  7608 ( 2018 ) Cite this article

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Previous studies looking at how Mind Wandering (MW) impacts performance in distinct Focused Attention (FA) systems, using the Attention Network Task (ANT), showed that the presence of pure MW thoughts did not impact the overall performance of ANT (alert, orienting and conflict) performance. However, it still remains unclear if the lack of interference of MW in the ANT, reported at the behavioral level, has a neurophysiological correspondence. We hypothesize that a distinct cortical processing may be required to meet attentional demands during MW. The objective of the present study was to test if, given similar levels of ANT performance, individuals predominantly focusing on MW or FA show distinct cortical processing. Thirty-three healthy participants underwent an EEG high-density acquisition while they were performing the ANT. MW was assessed following the ANT using an adapted version of the Resting State Questionnaire (ReSQ). The following ERP’s were analyzed: pN1, pP1, P1, N1, pN, and P3. At the behavioral level, participants were slower and less accurate when responding to incongruent than to congruent targets (conflict effect), benefiting from the presentation of the double (alerting effect) and spatial (orienting effect) cues. Consistent with the behavioral data, ERP’s waves were discriminative of distinct attentional effects. However, these results remained true irrespective of the MW condition, suggesting that MW imposed no additional cortical demand in alert, orienting, and conflict attention tasks.

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Introduction.

Compared to its small contribution to total body mass (~2%), the human brain contributes to a large proportion of the body’s overall energy consumption (20%). A large majority of the brain’s metabolism is associated with spontaneous activity (70–80%), with task evoked activity accounting for for only 5% of the energy consumption 1 . Estimates suggest that most of this spontaneous brain activity is associated with self-generated thoughts rather than external and task-related activity. This condition, known as mind wandering (MW), occupies most of our daily lives 2 , and may have important adaptive functions. Furthermore, there is now extensive evidence that suggests that MW plays an important role in processes such as autobiographic planning 3 and creativity 3 .

Despite the beneficial effects on creativity and daily planning, the perceptual decoupling that occurs during MW is thought to have significant attentional costs 4 . Interference between MW and attention may be due to the use of overlapping executive resources required in MW and attention 5 , 6 , 7 . This is further confirmed by brain imaging studies showing that MW tends to recruit not only the Default Mode Network, but also brain networks associated with executive functioning 8 .

However, there is increasing evidence that MW does not affect all types of attention tasks 7 . Experiments on the relationship between attention and MW have been producing inconsistent results 9 , 10 . These conflicting results may be explained by different concepts and methods used to assess both MW 11 and attention 12 . Some studies, define MW as an over inclusive category referring to all type of task-unrelated thoughts 7 . Contrarily, other studies provide a less inclusive definition that restricts MW to stimuli independent and task unrelated thoughts (e.g., thoughts dissociated either from the task or the current external conditions such as wandering about past memories or future plans) and excludes task related (e.g., thoughts on side aspects of the task such as task duration, concerns about overall performance, rumination over a mistake) or external (e.g., thoughts about task unrelated external and internal stimuli, such as light, temperature, hunger, thirst) distractors 13 .

The multiple approaches to assess MW can be divided in two major strategies: real-time and retrospective reports 14 . In real-time strategies, a thought-probe interrupts the task and prompts participants to report their ongoing conscious experience. These real-time strategies seem to be a reliable method for MW assessment, however they tend to interrupt the ongoing task 15 . To overcome this limitation, some studies use a retrospective strategy, collecting participants’ reports after the task by using either structured interviews or questionnaires 16 . This strategy, even though more prone to reliability issues, has the advantage of not interfering with the ongoing task. A good example of a retrospective strategy is the Resting State Questionnaire (ReSQ) 17 in which participants are requested to report the percentage of time spent on visual mental imagery, inner language, somatosensory, inner musical experience, and mental manipulation of numbers. Previous studies show that a significant percentage of individuals could be classified in a dominant MW mode 17 and that ReSQ is an effective method for assessing MW in resting state studies 18 .

Recently, MW research has evolved to use various experimental paradigms to approach the assessment of attention 15 , 19 . Acknowledging that attention is not a single mechanism led researchers to move away from the use of single sustained attention tasks and towards composite attention measures, such as the Attention Network Task (ANT) 20 , 21 . ANT is a computerized visual–motor task designed to assess three attentional networks: alerting, orienting, and conflict 22 , 23 . Alerting is defined as the process of reaching and maintaining a state of responsiveness to external stimuli and is assessed by looking at the facilitative effect of non-spatial informative cues (double cues) compared with the no cue condition. Orienting refers to the ability to select among multiple stimuli and is assessed by the facilitative effect of spatial informative cues compared with non-spatial informative cues. Finally, conflict refers to the executive monitoring of performance requiring inhibitory control, and is assessed by measuring the interference effect of incongruent flankers compared to congruent flankers.

At the behavioral level, studies looking at how MW interferes with the performance of distinct FA systems using the ANT 10 , 16 , showed that MW (i.e., defined as stimulus independent and task unrelated thoughts) did not impact overall performance and efficiency on each network assessed by the ANT (alert, orienting and conflict). One explanation for these results is that, while MW and focused attention thoughts (FA) share executive brain network resources, MW additionally recruits default mode network (DMN) resources 24 . The DMN is a networking connecting the medial frontal cortex with the posterior cingulate, precuneus and inferior parietal cortex. These brain regions show a higher activation during the rest condition and are usually deactivated when the individual performs a task requiring directing attention to an external stimulus 25 . Recent studies provided evidence that DMN may play an important role in high-level social cognitive processes such as self-referential thought 26 and social processing 27 .

Similar performances can be achieved by compensating executive losses with complementary brain network recruitment. Therefore, it is possible that MW may not only compete but also facilitate attention processes, such as attention recycling, dis-habituation, and mood regulation 28 . Noteworthy, several studies confirmed that increased functional connectivity in resting state networks (e.g., DMN) is associated with the properties of different event related potentials (ERP’s) proprieties 29 . Therefore it would be of interest to explore how MW modulates the electroencephalographic potentials found to be markers of the ANT.

Several neurophysiological studies have been looking at the ERP correlates associated with different ANT networks. For example, Neuhaus and colleagues 30 reported an increase in N1 amplitude in response to target onset following alert and orienting cues, thus suggesting that, at sensory stages of visual information processing, attentional processes underlying alerting and orienting networks operate concurrently. Moreover, they observed a frontal P3 increase and parietal P3 decrease for incongruent targets, pointing to a distinct topographic modulation of P3 amplitude by response inhibition. Later, Galvao-Carmona and colleagues 31 showed increased CNV amplitude to spatial cues before target onset, when compared with central and no cue conditions. They also found an increase amplitude in P1 for the spatial cue and increase in N1 components for the central cue after the target onset, and P3 amplitude for the congruent compared with incongruent targets. More recently, Williams and colleagues 32 also reported also an increase in N1 and CNV amplitude before the target presentation (as well as after the target for N1) for the double cue when compared with the no cue (alerting effect) and the spatial cue when compared with the central (orienting effect). Additionally, they found an increase in P1 after the target for the no cue condition, when compared with the double cue (alerting effect); and for the central cue, when compared with the spatial cue (orienting effect). The authors also reported an increase in N2 and a decrease of P3 amplitudes for incongruent targets. Kaufman and colleagues 33 confirmed the increase in N1 in response to the double cue when compared with the no cue (alerting effect) and to the spatial cue when contrasted with the center cue (orienting effect) after target presentation (particularly in young adults) along with a reduced P3 amplitude for incongruent targets (conflict effect).

Even though we are not aware of any studies looking into how MW impacts ANT performance as assessed by EEG, several studies using other attention paradigms suggest that MW affects several ERP components in FA tasks. For example, using a go/no-go task, Smallwood and colleagues 34 reported a decrease in P3 amplitude for no targets every time participants start mind wandering. In an oddball paradigm, Braboszcz and Delorme 35 found an increase in P2 amplitude during mind wandering for both oddball and standard auditory stimuli. Finally, Baird and colleagues 36 reported an attenuation of the P1 amplitude during mind wandering during a 0-back vigilance task.

Altogether, cue-locked CNV, N1 and target-locked N1 and P1 components seem to represent reliable ERP signatures of the alerting and orienting networks, while the target-locked P3 is a good marker for the conflict network. Studies using other experimental paradigms (e.g., go/no-go, odd-ball, vigilance task) found some of these components (e.g., P1, P3) to be also impacted by MW. Recently, several studies claimed the existence of prefrontal activity associated with alert or discriminative attention processes in distinct paradigms (e.g., pN, pP1) 37 . Research shows that those brain waves may be sensitive to a compensatory prefrontal load to maintain optimal performance in face of individual (e.g., aging) or situational constrains (e.g., conflict) 38 , 39 .

Building on these findings we hypothesize that a distinct cortical processing may be required to meet increasing attentional demands during MW. We tested if, given similar levels of ANT performance, individuals predominantly focusing on MW or FA show distinct neurophysiological processing, as evidenced by distinct properties of the following ERP’s during ANT: pN1, N1, pP1, P1, pN (cue -locked), and pN1, N1 pP1, P1, P3 (target-locked).

Participants

Thirty-three healthy individuals (22 female) with normal or corrected-to-normal vision took part in this study. Ages ranged from 18 to 40 years, with a mean of 23.45 years (SD = 5.01 years). Inclusion criteria were: (i) right handedness 39 ; (ii) no metal implants on the head; (iii) no history of neurological or psychiatric illness, electroconvulsive treatment, drug or alcohol abuse in the past year; (iv) no current medication for medical disorders that would impact electroencephalogram (EEG) morphology; (v) a score in Beck Depression Inventory (BDI) 40 ≤18. After a detailed description of the study, all participants provided signed informed consent, and the study was carried out under The Code of Ethics of the World Medical Association (Declaration of Helsinki). The study was approved by the Institutional Review Board of the Mackenzie Presbyterian University and by the National Ethics Committee (SISNEP, Brazil), and all participants had provided written informed consent.

Attention network test (ANT)

The ANT is a computerized task, designed to assess three attentional networks: alert, orienting, and conflict. Participants are required to focus on a central fixation cross and identify if the target (i.e., central arrow appearing below or above a fixation cross) is pointing right or left. The targets are preceded by three cue conditions: a spatially informative cue presented above or below the fixation cross, a center or double time informative cue, and a no cue condition. The target may be presented alone or accompanied by three distinct flankers: arrows pointing in the same direction of the target (congruent condition), pointing in an opposing direction (incongruent condition), or traces without arrows (neutral condition).

ANT was programed and presented via E-Prime 2.10 (Psychology Software tools, Sharpsburg, PA, US) in a desktop computer, using the following parameters: (1) a fixation cross appeared and remained in the center of the screen throughout the entire trial; (2) after a random duration varying between 400 and 1600 ms, a cue (none, center, double or spatial cue) appeared for 100 ms; (3) after a fixed period of 400 ms, the target (the center arrow) and flankers (congruent, incongruent or neutral flankers) were presented until the participant responded but with a time limit of 1700 ms (participant’s response was done by pressing either the right or the left side of the computer mouse with their dominant hand); (4) after the response, the target and flankers were replaced by the central fixation cross (the time lapse between the onset of the target and the start time of the next trial comprised 3500 ms minus both the participant’s reaction time and the duration of the first fixation cross). A session consisted of seven blocks: one full-feedback practice block and six experimental blocks without feedback. Each experimental block included 48 trials (4 cue conditions × 2 target locations × 3 flanker conditions × 2 repetitions). In every block, trials were presented in a random order (see Fig.  1 ).

Attention Network Task - Experimental paradigm.

The ANT allows the computation of three attentional systems: alerting, orienting and conflict. In the present study, the alerting network is defined by the facilitative effect of the double cue compared with the no cue condition. The orienting network refers to the facilitative effect of the spatial informative cue (above and below the fixation cross) compared with central cue. The conflict network expresses the interfering effect of incongruent flankers compared with congruent flankers.

Even though researchers are still searching for more reliable methods of computing ANT network effects 41 , 42 , studies show that these three attentional components are supported by different neuroanatomical networks 22 and are associated with distinct genetic profiles 43 .

Mind Wandering Task (MW)

Upon ANT completion, all participants were asked to fill out an adapted version of the Resting State Questionnaire (ReSQ) 17 and report the percentage of time spent in the following categories of mental activity: focusing on the task (FT); visual mental imagery - seeing something in thought (IMAG); inner language - thinking in words with your own voice without overt production (LANG); somatosensory awareness - paying attention to a sensory aspect of the body (SOMA); inner musical experience - experiencing a melody and/or a rhythm in thought (MUSI); and mental processing of numbers - arithmetic processing, counting, estimation of the time to the end (NUMB). The sample was divided in two groups based on their responses to the ReSQ using a modified version of the half split method. Participants that report to have been over 60% focused on task were classified as the “focused attention” (FA) group (n = 13), while those reporting over 60% in the remaining conditions were classified in the “mind wandering” (MW) group (n = 20). Participants were excluded from further analysis if they did not reach a score of 60% in one of the categories (in the current study all participants were included). MW and FA groups did not differ in terms of sex (Χ 2  = 0.06, p = 0.80), age [ t (31) = 0.07, p  = 0.93] and years of education [ t (31) = 1.26, p  = 0.22]. The average percentages for each ReSQ category were: FT–45.76%; IMAG–14.32%; LANG–18.53%; SOMA–7.71; MUSI–11.29%; NUMB–2.09%.

After providing signed informed consent and before the experimental trials, participants went through the following steps: (1) instructions about the overall procedure; (2) a practice block of the ANT with feedback; (3) six ANT experimental blocks synchronized with EEG acquisition (see Fig.  1 ); (4) Completing the ReSQ.

EEG data acquisition and analysis

A high-density 128 geodesic sensor net (Electrical Geodesic) was used for EEG data acquisition. Impedances were kept below 40 kΩ at the beginning of the experiment, and monitored throughout the entire session. EEG was sampled at 250 Hz, band-pass filtered between 0.01–100 Hz (Notch filter at 60 Hz), and stored for later analysis. EEG recordings were referenced to Cz and later referenced offline to the average-reference. The following steps were used for EEG processing: (1) ERPs for correct trials were segmented into epochs that were time-locked according to cue onset and target onset (100 ms before and 600 ms after either the cue or target onset); (2) artifact detection (difference >140 μV between channels above and below the eyes, difference >55 μV between channels near the outer canthi, or one or more channels exceeding an amplitude of 200 μV); (3) re-referencing of scalp potentials to the average reference; (4) baseline correction from 100 ms before each segment. Epochs containing artifacts due to eye blinks, ocular and head movements were automatically rejected.

After visually inspecting the grand average ERP waveforms, cue-locked P1, pN1, N1, pP1 and pN and the target-locked, P1, pN1, N1, pP1 and P3 ERP components were selected for further analyses. Electrode sites shown in Fig.  2 were chosen according to previous research. Adaptive mean amplitudes were analyzed within the 90–150 ms time window after cue and/or target for P1 (at O1, O2, and OZ) and for pN1 (FP1, FP2, FPz), 150–250 ms for pP1 (at FP1, FP2, FPz), 160–260 ms for N1 (at O1, O2, and OZ and Pz and adjacent electrodes), and 300–450 ms for P3 (at O1, O2, and OZ and Pz and adjacent electrodes). A slow negative potential (i.e., CNV like) was most prominent in prefrontal (FP1, FP2, FPz) and frontal scalp regions (Fz and adjacent electrodes) between 350–500 ms after cue onset. Due to its topography, we will refer to this type of wave, now on, as prefrontal negativity (pN).

Scalp sites for cue and target locked ERP’s.

In every condition, at least 70% of artifact-free segments were included in the individual ERP averages (No Cue = 89.56 ± 7.32; Center Cue = 87.88 ± 8.45; Double Cue = 87.75 ± 9.18; Spatial Cue = 86.40 ± 9.99; Congruent = 88.98 ± 7.28; Incongruent = 87.15 ± 9.06; Neutral = 87.56 ± 9.20). The groups did not differ in terms of the number of artifact-free epochs included in the individuals grand-averages per condition [ F (1,192) = 1.42, p  = 0.21].

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

Relationship Between Focused Attention and Mind Wandering –Behavioral Results

For the analyzes of behavioral results, two repeated-measures analysis of variance (ANOVAs) were performed with ANT’s reaction time (RT) or accuracy score as dependent variables and type of cue (double cue, center cue, no cue and spatial cue), type of target (congruent, neutral, incongruent) as the within-subject factors, and mind-wandering (MW, FA) as between-subjects factor. For RT there was a significant main effects for target [ F (1,31) = 503.31, p  < 0.001, ηp 2  = 0.94] and cue conditions [ F (3,93) = 128.49, p  < 0.001, ηp 2  = 0.81] as well as cue*target interaction [ F (3,93) = 10.23, p  < 0.001, ηp 2  = 0.25]. No significant effects were found for mind-wandering [ F (1,31) = 1.34, p  < 0.26, ηp 2  = 0.04] and for the interactions between cue*mind-wandering [ F (3,93) = 0.37, p  < 0.77, ηp 2  = 0.01], target*mind-wandering [ F (1,31) = 0.16, p  < 0.69, ηp 2  = 0.01] and cue*target*mind-wandering [ F (3,93) = 1.43, p  < 0.24, ηp 2  = 0.04]. Bonferroni post-hoc tests showed significant differences between all conditions ( p  < 0.001) except for the following conditions ( p  > 0.05): double-congruent and center-congruent; no cue-congruent and spatial-incongruent; and between no cue-incongruent and center incongruent (see Fig.  3 ).

ANT Reaction times ( a ) and accuracy ( b ) in the FA and MW groups for different cue and target conditions (mean ± SD).

In terms of accuracy, significant main effects were found for target [ F (1,31) = 39.47, p  < 0.001, ηp 2  = 0.56] and cue conditions [ F (3,93) = 3.06, p  = 0.03, ηp 2  = 0.09], but not for the cue*target interaction [ F (3,93) = 1.98, p  = 0.12, ηp 2  = 0.06]. Again, no significant effects were found, for mind-wandering [ F (1,31) = 1.81, p  = 0.19, ηp 2  = 0.06] or for the interactions between cue*mind-wandering [ F (3,93) = 0.55, p  = 0.64, ηp 2  = 0.02], target*mind-wandering [ F (1,31) = 2.67, p  = 0.11, ηp 2  = 0.08] and cue*target*mind-wandering [ F (3,93) = 0.18, p  = 0.91, ηp 2  = 0.01]. Bonferroni post-hoc tests showed that the only significant difference was between center and no cue conditions ( p  = 0.05).

Relationship Between Focused Attention and Mind Wandering –ERP Results

For each component, mixed ANOVAs were used with adaptive mean amplitude (µV) at the selected electrodes as the dependent variable, type of cue as within-subjects factor (double cue, center cue, spatial cue – for cue locked components; double cue, center cue, spatial cue, no cue – for target locked components) and mind-wandering (MW, FA) as between-subjects factor. For N1 and P3 scalp region (occipital, parietal) was also analyzed as within-factor variable. In the specific case of P3, type of cue was replaced by target type as within-factor variable. Paired comparisons with Student’s t-tests were used to test the direction of significant main or interaction effects. An alpha level of 5% was adopted for all statistical tests. Figures  4 , 5 , 6 , 7 and 8 present the grand-averaged ERP waveforms and topographical maps for all cue locked (Figs  4 and 5 ) and target locked components (Figs  6 , 7 and 8 ).

Grand averages for cue locked ERP components.

Topographical maps for cue locked ERP components.

Grand averages for target locked ERP components – alerting effects.

Grand averages for target locked ERP components – orienting effects.

Topographical maps for target locked ERP components.

P1 Component

Regarding the cue-locked P1, no significant effects were found for either mind-wandering [ F (1,31) = 2.94, p  = 0.10, ηp 2  = 0.09] nor for the interaction cue*mind-wandering [ F (2,62) = 0.14, p  = 0.87, ηp 2  = 0.005]. However, a significant main effect was found for cue condition [ F (2,62) = 14.95, p  = 0.001, ηp 2  = 0.33]. Paired-comparisons between double, center and spatial cues showed significant differences between center and spatial cue [ t (32) = −4.26, p  < 0.001], as well as between center and double cue [ t (32) = 2.69, p  = 0.01] but not between the double and spatial cue [ t (32) = −1.92, p  = 0.07]. As illustrated in Fig.  4 , both double and the spatial cues resulted in larger P1 amplitudes compared to the center cue.

For the target-locked P1, again there were no significant effects either for mind-wandering [ F (1,31) = 2.21, p  = 0.15, ηp 2  = 0.07] nor for the interaction cue*mind-wandering [ F (3,93) = 0.14, p  = 0.94, ηp 2  = 0.01]; however there was a significant main-effect for cue condition [ F (3,93) = 5.88, p  = 0.001, ηp 2  = 0.16]. As illustrated in Fig.  6 , paired-comparisons revealed a significant alerting effect [no cue vs double cue; t (32) = 4.04, p  < 0.001] with increased P1 amplitude for the no cue condition. No significant orienting effects were found for target-locked P1 [center vs spatial cue; t (32) = −0.82, p  = 0.42] (see Fig.  7 ).

pN1 Component

The analysis for cue-locked pN1 component showed no significant effects for mind-wandering [ F (1,31) = 0.7, p  = 0.41, ηp 2  = 0.02] or cue*mind-wandering interaction [ F (3,93) = 1.7, p  = 0.17, ηp 2  = 0.05]. There was a significant main effect for the cue condition [ F (2,62) = 12.92, p  = < 0.001, ηp 2  = 0.29]. Paired comparisons showed significant differences between center - double cue [ t (32) = 4.5, p  < 0.001] and center-spatial cues [ t (32) = 3.57, p  = 0.001]. Both double and the spatial cues resulted in larger pN1 amplitudes compared to the center cue.

A similar result was found for the target-locked pN1 component, with a significant main effect for cue [ F (3,93) = 3.06, p  = 0.03, ηp 2  = 0.09] but no effects for mind-wandering [ F (1,31) = 1.77, p  = 0.19, ηp 2  = 0.05] or cue*mind-wandering interaction [ F (3,93) = 0.87, p  = 0.46, ηp 2  = 0.03]. Paired comparisons showed significant differences between no cue - double cue [ t (32) = 2.44, p  = 0.02] and no cue - spatial cue [ t (32) = −2.28, p  = 0.03]. As shown in Fig.  6 , the no cue condition resulted in a larger pN1 when compared with double and spatial cues.

N1 Component

For the cue-locked N1, significant effects were found for cue [ F (2,62) = 32.34, p  < 0.001, ηp 2  = 0.51], scalp [ F (1,31) = 28.67, p  < 0.001, ηp 2  = 0.48] and cue*scalp region interaction [ F (2,62) = 8.1, p  = 0.001, ηp 2  = 0.51], but without significant effects for mind-wandering [ F (1,31) = 0.51, p  = 0.48, ηp 2  = 0.02], cue*mind-wandering [ F (2,62) = 0.10, p  = 0.90, ηp 2  = 0.003], scalp region*mind-wandering [ F (1,31) = 0.01, p  = 0.97, ηp 2  < 0.001] nor for cue*scalp region*mind-wandering [ F (2,62) = 0.62, p  = 0.54, ηp 2  = 0.02]. Paired-comparisons confirmed significant larger N1 amplitude for the double cue when compared with center and spatial cues in parietal [double vs. center, t (32) = 5.51, p < 0.001; double vs. spatial, t (32) = −8,9, p < 0.001] and occipital regions [double vs. center, t (32) = 5.84, p < 0.001; double vs. spatial, t (32) = −9,9, p < 0.001].

The analysis of target-locked N1 revealed again significant main-effects for cue condition [ F (3,93) = 29.87, p  < 0.001, ηp 2  = 0.49] and scalp [ F (1,31) = 8.89, p  = 0.006, ηp 2  = 0.22], as well as the interaction between cue*scalp [ F (3,93) = 8.69, p < 0.001, ηp 2  = 0.22]. No significant main effects were found for mind-wandering [ F (1,31) = 0.147, p  = 0.704, ηp 2  = 0.005], and interactions mind-wandering*cue [ F (3,93) = 2.15, p  = 0.99, ηp 2  = 0.03], mind-wandering*scalp region [ F (1,31) = 0.002, p  = 0.96, ηp 2  < 0.001], mind-wandering*cue*scalp region [ F (3,93) = 0.48, p  = 0.70, ηp 2  = 0.2]. Table  1 shows the results of paired-comparisons regarding the orienting (no cue vs. double cue targets) and alerting effects (center vs. spatial cues targets) for parietal and occipital regions. N1 adaptive mean amplitudes were larger for spatial and double cues as compared to center (orienting effect) and no cue (alerting effect).

pP1 Component

For cue-locked pP1 significant main effects were found for the cue condition [ F (2,62) = 15.99, p  = < 0.001, ηp 2  = 0.34], but not for mind-wandering [ F (1,31) = 0.01, p  = 0.91, ηp 2  = 0.05] or cue*mind-wandering interaction [ F (2,62) = 2.57, p  = 0.09, ηp 2  = 0.08]. Paired comparisons showed pP1 larger adaptive mean amplitudes for the double condition when compared with center cue [ t (32) = −5.65, p  < 0.001] and spatial cue [ t (32) = 5.42, p  < 0.001].

Concerning target-locked pP1, again significant effects were found for cue [ F (3,93) = 7.94, p  = < 0.001, ηp 2  = 0.20], but not for mind-wandering [ F (1,31) = 1.97, p  = 0.17, ηp 2  = 0.06] nor cue*mind-wandering interaction [ F (3,93) = 0.56, p  = 0.64, ηp 2  = 0.02]. Post-hoc comparison showed smaller adaptive mean amplitudes for the no cue when compared with the all other cue conditions [no cue and center cue, t (32) = 3.9, p  < 0.001; no cue and double cue, t (32) = 3.17, p  = 0.003; no cue and spatial cue, t (32) = −4.1, p  < 0.001].

pN Component

For cue-locked pN, significant main effects were found for the cue [ F (2,62) = 10.22, p  < 0.001, ηp 2  = 0.25], but not for mind-wandering [ F (1,31) = 2.4, p  = 0.13, ηp 2  = 0.07] nor for the interaction cue*mind-wandering [ F (2,62) = 0.52, p  = 0.60, ηp 2  = 0.02]. Paired-comparisons revealed significant larger adaptive mean amplitude for the spatial cue when compared with all the other cues [double cue, t (32) = 4, p  < 0.001; center cue, t (32) = 3.4, p  = 0.002].

P3 Component

Regarding the target-locked P3, significant main-effects were found for the target condition [ F (2,62) = 25.31, p  < 0.001, ηp 2  = 0.45], scalp region [ F (1,31) = 77.30, p  < 0.001, ηp 2  = 0.71] as well as the interaction target*scalp region [ F (2,62) = 8.53, p < 0.001, ηp 2  = 0.22]. Again, no significant main effects were found for of mind-wandering [ F (1,31) = 0.38, p  = 0.54, ηp 2  = 0.01], and interactions mind-wandering*target [ F (2,62) = 0.25, p  = 0.77, ηp 2  = 0.008], mind-wandering*scalp region [ F (1,31) = 3.19, p  = 0.08, ηp 2  = 0.09], mind-wandering*target*scalp region [ F (2,62) = 1.56, p  = 0.22, ηp 2  = 0.05].

Paired-comparisons were performed between congruent, incongruent and neutral targets for parietal and occipital regions. Table  2 shows that P3 adaptive mean amplitudes were significantly larger for congruent and neutral as compared to incongruent targets in the parietal and occipital electrodes (conflict effect). We found larger P300 components for the parietal as compared to the occipital electrodes for congruent ( t32  = −7.98, p  < 0.001), incongruent ( t32  = −7.59, p  < 0.001) and neutral ( t32  = −8.23, p  < 0.001).

The present study investigates the modulatory effects of MW thoughts on the ERP correlates of attention networks (i.e., alerting, orienting and conflict). As expected, ANT’s behavioral results showed evidence of significant alerting, orienting and conflict effects. Participants were slower and less accurate when responding to incongruent than to congruent targets (conflict effect) and benefit from the presentation of double (alerting effect) and spatial cues (orienting effect). These findings remain true irrespective of the MW condition. Participants from the MW group performed similarly to the FA group, showing the same attention effects (alerting, orienting, and conflict). This data is consistent with previous findings showing the lack of interference of MW on ANT’s overall performance or any of the attention networks 10 , 16 . A previous study found a negative correlation between MW with somatosensory awareness content and the alerting network, however this was not generalized across all the study population 16 . This MW content is probably more associated with “external distractions” than to pure MW, a category that has been found to interfere with ANT accuracy 10 .

The central aim of the present study was, however, to explore if MW impacts the ERP correlates of alert, orienting and conflict attention tasks. Overall, the ERP’s analyzed were discriminative of the three attentional effects (alerting, orienting, conflict) irrespective of the MW condition.

For cue-locked P1 we found an amplitude increase with the information power of the cue (significant increases from center cue, to double cue and spatial cue, and approaching significance between double and spatial cues). P1 is considered an early marker of visual attention generated in the extrastriate region of the visual cortex, showing an enhanced amplitude as the informative power of the cue increases 44 , 45 . When we moved to a target locked P1 we found a decrease in P1 for alerting along with a lack of an orientation effect. This is consistent with Williams 32 and colleagues study, showing an increased amplitude in the no cue condition compared with the double cue condition. However, Galvao-Carmona and colleagues reported the opposite finding of an increased P1 orienting effect and no significant alerting effects. Williams 32 considered that the discrepancy in these findings may be associated with the different scalp locations where the effects were found: averaged at P3, Pz, P4, O1, Oz, O2 in Williams study and PO5 and PO6 in Galvao-Carmona study. Our findings support this explanation as we analyzed the P1 component at O1, Oz and O2. Furthermore, it seems that the spatial distribution of the P1 at the occipital sites reflects the absence of an alerting cue by an enhanced no-cued target P1. It is known that the delay between cue and target also impacts the amplitude of the P1 component 46 , 47 . Our ANT protocol was similar to the one used by Williams (100 ms for the cue followed by a 400 ms interval before the target) while Galvao-Carmona used a larger delay between the cue and the target (150 ms for the cue followed by 1000 ms before the target). Therefore, P1 seems to be a specific cue elicited marker for the amount of the cue information, dependent also of the cue-target delay as well as spatial distribution. Summing up, both Williams and our current experiment suggest that the lack of cue information enhances the alert at the early stages of visual processing as reflected by an enhanced P1 at occipital electrodes.

N1 has been described as an early visual attention component, generated at both parietal and occipital regions. The present study confirmed that target locked N1 is a good marker for both the alerting and the orienting effects, with increased amplitudes for the double cue (alerting effect) and spatial cues (orientation effect) both at occipital and parietal regions (even though more evident in the occipital region). Similar findings were reported in previous studies 32 , 33 . The present results are in line with Neuhaus 30 and colleagues showing an increase in N1 amplitudes for the alerting and orienting conditions as well as significant interaction with electrode site (alerting effects mostly observed at parietal electrodes and orienting effects at occipital and parieto-occipital electrodes). Consistent with our data, the author found that increases in N1 amplitude were particularly evident after alerting cues.

Similar to the studies reported above, N1 amplitude increased in the following order: no cue, center cue, spatial, double cue. This finding confirms that N1 is more sensitive to the alerting effects and that the orientation effect found may result from the relative interdependence of the networks as measured by the ANT (e.g., orienting cues have also alerting effects) 42 , 48 .

As anticipated, prefrontal activity ERP markers were discriminative for distinct attentional effects but, once again, irrespective of the MW condition. Therefore, there was no evidence for compensatory prefrontal activity as expressed in pN1, pP1 or pN. However, larger pN1 and pP1 amplitudes were associated with increments in cue informative power (double and spatial cues when compared to the center cue) for the cue-locked interval. Moreover, alerting, but no orienting effects, were present in pN1(smaller amplitude in the double cue when compared with no cue condition) and pP1 (smaller amplitude in the no cue when compared with double cue condition). These findings bring additional confirmation for the hypothesis that these markers of prefrontal activity reflect early perceptual processes of stimulus evaluation. Previous studies have suggested that these ERPs are detected in stimulus locked but not response locked paradigms 37 .

Similar findings were present in the cue-locked pN with an increased negativity, but in this case only in response to the spatial cue. The pN is a slow negative potential (350–500 ms after cue onset) with a medial frontal scalp distribution and has been thought to reflect proactive inhibitory control 49 . This ERP is dependent on a bilateral activation of the pars opercularis , and occurs of weather the reaction following stimulus detection is inhibitory or not 50 . The increased negativity in response to the spatial cue suggests an early sensitivity (between the warning cue and the target) to perceptual interpretation, probably due to proactive inhibitory processes involved in orienting attention.

Reduced P3 amplitude was found for the incongruent targets when compared with the congruent and the neutral conditions. These P3 effects for all targets were more clear in parietal regions. This confirms the interfering effects of incongruent targets in attention performance as evidenced by a decreased of accuracy and increased in RT for incongruent targets. Other studies have found similar reduced P3 amplitudes at parietal sites for incongruent targets 30 , 31 , 32 , 33 . Our finding of a P3 reduction for the incongruent target when compared with congruent and neutral targets, but not between the congruent and neutral targets, suggests that the P3 effects seem to be specific for situations of attention conflict. Neuroimaging studies showed an increased activation of the anterior cingulate (a region involved in conflict resolution) for incongruent tasks 22 . The anterior cingulate has been identified as a core source for the P3 51 .

Finally, consistent with the lacking of MW effects on ANT performance, there were no differences between the MW and FA groups in pN, P1, and N1 ERP’s for orienting and alerting cues and P3 for incongruent targets, suggesting that MW is still compatible with effective early alerting and orienting visual attention processes and resolution of attention conflict.

The aim of the present study was to test if the lack of ANT task costs in MW could be explained by an increased cortical demand illustrated in the modulation of cortical activity. Contrary to previous studies, we found that alert, orienting and conflict attention ERP’s markers were independent of MW condition. However, none of those studies used the ANT to assess attention. For example, modulation of P3 34 , P2 35 and P1 36 , was evident when individuals mind wander in response to non-targets or targets in vigilant tasks (SART, 0-back vigilant), or passive odd-ball stimuli. More important, in these studies, MW was broadly defined, including both task-related and internal distractors (e.g., losing track of their thoughts during a breath count task 35 ; thinking about anything unrelated to the task 36 ; “tuned out” – consciously away from the task or “zoned out” – unconsciously off-task 34 ). It is possible that the modulation of cortical activity reported in these studies, is associated with the presence of external and task distractors rather than MW per se . Interesting to note that, contrary to our study, previous research has reported cost effects for off-task thoughts. For example, Baird and colleagues found significant increases in RT in responding to non-frequent targets in a 0-back vigilance tasks when preceded by off-task thoughts 36 .

The present findings bring further evidence to the assumption that the level of processing required for ANT performance is compatible with mind wandering. As suggested by some authors, individuals with good executive resources are able to mind-wander without significant impact on their attention in non-demanding tasks at the behavioral level 28 . The present study shows that this may be also true at the neurophysiological level. That is, no evidence was found for additional cortical demand in MW for identical performance on the ANT. It is possible that, with increased task demand, additional cortical recruitment would be required even before response costs are evident. Future studies should test this possibility by manipulating ANT task-related demand.

In the current ANT paradigm a fixed 400 ms cue-target interval was used, precluding the analysis of different Stimulus Onset Asynchrony’s (SOA’s) in ANT performance and ERP correlates. For example, studies show that at the behavioral level, the classical cue-target effect is evident for intermediate and long SOAs, whereas for short SOAs, there was a time cost associated with valid cues 52 . Additionally, several studies confirm the influence of SOA’s on ERPs responses. Indeed, ERPs seem to be depend on different SOA interval 53 , order 54 and experimental conditions 55 . Therefore it would be interesting to test the modulation of MW in ANT at the behavioral and neurophysiological levels controlling for different SOA’s.

Despite the consistency between the behavioral and ERP data, the present results should be interpreted in light of some methodological limitations. One of the more complex issues in this research domain is finding a reliable way for assessing MW, namely the choice between the use of real time or retrospective strategies 14 . Real time probes are more effective in providing a reliable account of interfering thoughts. In the case of the ANT, in which different trials within each block are measuring different network effects, an effective real-time probe would be required for each trial. However, this would interrupt the sequence of trials within each block, interfering with the ANT paradigm. In previous studies we opted for a thought probe encompassing the whole block and not only the preceding trial 10 . However, this strategy would be prone to recency effects, with participants eventually biasing their reports towards the immediate trial preceding the thought probe. This is the main reason underlying the choice for the retrospective offline report on the content of interfering thoughts in this study. This strategy has the advantage of not interfering with the attention task, being also more resistant to recency effects. Nevertheless, we cannot be sure about the reliability of participants’ retrospective offline reports. Some authors recommended the priming of MW or FA conditions 56 . For example, a recent study showed that the stimulation of the left dorsolateral prefrontal cortex with transcranial direct current stimulation increased MW. Consistent with our data, rather than having a negative impact, the increase in MW induced a small attention improvement.

In conclusion, at the behavioral level, no significant interfering MW effects were found in ANT performance (reaction time, accuracy and network effects). Consistent with these findings, ERP markers associated with distinct attentional effects (alert, orienting, conflict) were not modulated by the MW condition. Future studies should try to replicate these results with larger samples, controlling for task-related demands and SOA’s while experimentally priming MW/FA conditions.

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Acknowledgements

Óscar F. Gonçalves was funded by the Brazilian National Counsel for Scientific and Technological Development (CNPq) as a Special Visiting Researcher of the Science Without Borders program (401143/2014-7). Paulo S Boggio was funded by a CNPq researcher fellowship (311641/2015-6). Olivia Morgan Lapenta and Tatiana Conde were supported by two postdoctoral grants from CNPq (150249/2017-9 and 152358/2016-1). Sandra Carvalho was funded by the Portuguese Foundation for Science and Technology (FCT) with the grant IF/00091/2015. Gabriel Rêgo was supported by a PhD grant from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP-2015/18713-9). This work was partially supported by FEDER funds through the Programa Operacional Factores de Competitividade – COMPETE and by national funds through FCT – Fundação para a Ciência e a Tecnologia (P2020-PTDC/MHC-PCN/3950/2014).

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O.F.G. and P.B. designed the study, G.R., S.C. and J.L. prepared the experimental paradigm, O.L. and T.C. collected data. G.R., O.L., T.C., P.B. and O.F.G. participated in data analysis. All authors discussed the results and commented on the manuscript.

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Correspondence to Óscar F. Gonçalves .

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Gonçalves, Ó.F., Rêgo, G., Conde, T. et al. Mind Wandering and Task-Focused Attention: ERP Correlates. Sci Rep 8 , 7608 (2018). https://doi.org/10.1038/s41598-018-26028-w

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DOI : https://doi.org/10.1038/s41598-018-26028-w

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Mind wandering in children: Examining task-unrelated thoughts in computerized tasks and a classroom lesson, and the association with different executive functions

Affiliations.

• 1 Department of Neuropsychology & Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands. Electronic address: [email protected].
• 2 Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands.
• PMID: 30562634
• DOI: 10.1016/j.jecp.2018.11.013

Mind wandering is associated with worse performance on cognitively demanding tasks, but this concept is largely unexplored in typically developing children and little is known about the relation between mind wandering and specific executive functions (EFs). This study aimed, first, to measure and compare children's mind wandering in controlled computerized tasks as well as in an educational setting and, second, to examine the association between mind wandering and the three core EFs, namely inhibition, working memory, and set shifting/switching. A total of 52 children aged 9-11 years performed a classroom listening task and a computerized EF battery consisting of flanker, running span, and attention switching tasks. Mind wandering was measured using online probed and/or retrospective self-reports of task-unrelated thoughts (TUTs) during task performance. Children reported TUTs on 20-25% of the thought probes, which did not differ between classroom and EF tasks. Regression models, hierarchically adding the three core EFs, accounted for a small but significant portion of variance in TUT frequency when measured in class and retrospectively after EF tasks, but not when measured online in EF tasks. Children with worse inhibition were more prone to mind wander during classroom and EF tasks. Lower attention switching accuracy also explained variation in retrospectively reported TUTs during EF tasks. Working memory was not a significant predictor. These results suggest that mind wandering is common and reliably measurable in children in controlled computerized and educational tasks. Lower executive control abilities predict more frequent mind wandering, although different EFs are related to mind wandering in diverse tasks/measures.

Keywords: Educational setting; Executive function; Inhibition/interference control; Mind wandering; Task-unrelated thought; Typically developing children.

Copyright © 2018 Elsevier Inc. All rights reserved.

• Attention / physiology*
• Auditory Perception / physiology*
• Executive Function / physiology*
• Inhibition, Psychological
• Netherlands
• Retrospective Studies
• Self Report
• Surveys and Questionnaires
• Task Performance and Analysis*